Dilip

Engineering @Uber

[dara khosrowshahi @dkhos]

V excited about this partnership and giving more Prime Eaters the opportunity to save$s.

[dara khosrowshahi @dkhos]

U4B has been one of the stars in our portfolio and thrilled to bring on the @ezCater team to continue the growth and increase penetration w/SMBs. More reasons to use @Uber!

[Andrew Macdonald @andrewgordonmac]

From food delivery to grocery, retail and now catering - we’re expanding what you can get through Uber

Excited to welcome @ezCater to Uber, helping restaurants grow and @Uber_Business customers better serve their employees and clients

[Uber @Uber]

We invited a group of seniors to Uber HQ and told them they could ask us anything. There were questions. There were badges. There were diplomas. And yes, everyone passed. 🎓

[Lenny Rachitsky @lennysan]

Best SF billboard goes to this estate planning lawyer

[Dilip @pdilip]

@0xSigil @Mascobot Do you have an invite? Would like to try it out.

[Dilip @pdilip]

@AlexFengzh Asus also comes with a built in KVM

[Dilip @pdilip]

@AlexFengzh Did you end up getting the 2022 or 2026 version? Debating the 2x cost

[Dilip @pdilip]

@AlexFengzh Have you tried the Asus Pro Art 5K line?

[Balaji Krishnamurthy @_balaji_km]

Aggregate stats often result in misleading conclusions. Here’s a good example from @a16z (https://t.co/QAks46tWSa) that implies Uber’s prices in the US are up significantly more than Lyft.

At Uber, we measure this particular metric every single week. But to get to the true signal, we always control for mix effects from trip type and geography. We measure price quotes on a standard product (in this case UberX vs. Lyft Standard) on identical trips (same origin/destination/time of day combinations), and we call this metric NPI (normalized price index). Our philosophy consistently has been to price as affordably and as competitively as possible, with the NPI near 1.0 at all times. On an apples to apples basis, that story remains remarkably consistent in the US.

Even though the overall assessment is directionally accurate, in not controlling for mix effects, A16Z/Gridwise come to an inaccurate conclusion. In fact, it shows the success of our deliberate strategy to both innovate with new products and expand our footprint into sparser markets. As a result, (1) Uber skews more toward higher-priced premium products, as consumers are increasingly opting into our newer premium products, and (2) Uber trips skew longer, and the mix of trips >15km has increased meaningfully driven by the faster growth in suburban and sparser markets.

In other words, we are serving consumers with better products, and we are serving more consumers than ever before in newer markets that have been significantly underpenetrated for the ridehailing category.

[Praveen Neppalli @praveenTweets]

Since I previously shared how we are scaling AI usage so quickly without costs rising at the same rate, there’s been a lot of interest in the engineering details.

So I asked the team to open up the hood and share what we’ve learned.

One thing we’ve always been very good at @Uber is understanding the economics of a system at a deep level. And I’ve always believed that constraints drive innovation. We took the opportunity to turn the cost problem into an engineering problem. We broke AI spend down into its underlying levers and started systematically attacking each one:

• Scaling vendor-neutral managed agents
• Building Uber SWE benchmarks to measure what actually works
• Optimizing prompt caching
• Making tool and MCP usage more efficient
• Grounding trajectories in our context graph
• Building efficient, reusable agent skills
• Giving engineers real-time cost visibility and optimization tips

There’s a lot more work to do in this space: scaling more autonomous agents, dynamic model routing, a self-evolving context graph, and continuously updating efficient agent skills. There's still a tremendous amount to invent here, and we’ll keep sharing what we learn as we build and deploy it at Uber.

This is an incredible time to be an engineer. The economics of software are changing, the way we build is changing, and engineers have an opportunity to help define what comes next.

Read the deep dive here: https://t.co/y1egvGPqWa and let us know what you think.

[Satya Nadella @satyanadella]

https://t.co/vLmiBKTtX3

[Zara Zhang @zarazhangrui]

If you've adopted AI at your company but haven't seen any tangible results, read this 1990 article: "The Dynamo and the Computer" by Paul David.

When electricity first arrived, factories that "adopted" it barely got faster. They just swapped the steam engine for an electric one and ran everything else exactly as before: same machine layout, same workflow, same management. Electricity in, no real gains out.

The most common mistake with any new technology is to drop it into the old organization and then declare the transformation done.

The real leap came decades later, when each machine got its own small motor. Suddenly machines no longer had to be lined up around one central drive shaft. They could be rearranged around the actual flow of work.

The productivity gains didn't come from electricity. They came from REDESIGNING THE ENTIRE FACTORY around it.

AI is the same. Bolting it onto your existing process gets you a faster steam engine. The payoff comes when you redesign the work itself.

(link to paper in comments)

[Celine Halioua @celinehalioua]

Loyal has raised a $100M Series C from age1, Baillie Gifford, existing investors, & more

this brings Loyal's total funding to over a quarter of a billion dollars

we are building the longevity pharma, starting with dog longevity. thanks @FastCompany for the exclusive ->

[Andrej Karpathy @karpathy]

A few random notes from claude coding quite a bit last few weeks.

Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.

IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits.

Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.

Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.

Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage.

Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.

Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.

Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements.

Questions. A few of the questions on my mind:
- What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*.
- Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
- What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
- How much of society is bottlenecked by digital knowledge work?

TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

[diplo @diplo]

I was 20 when I first came to India with nothing but a restless mind and an old Enfield I bought from a friend in Delhi who taught me to ride in one dusty afternoon. He took my money, flew back to Florida, and left me with one rule: don’t hit a cow, and only ride between 2–6 a.m. if you want to survive the heat and smog. Somehow, that became a philosophy for everything that followed.

I crossed the country like a kid inside a dream — Calcutta to Delhi to Rishikesh — sleeping on the bike when I had to, chasing chai stalls to stay awake, tossing the bike on trains when I could afford it. I swam in the Ganges, did yoga with elders who moved like water, bought vinyl in back-alley shops, fell in love the way only your twenties let you, and wrote long confusing emails to my mom from glowing village internet cafés.

In Gujarat I stopped long enough to help with earthquake relief, eat thalis in strangers’ homes, and learn “Kem Cho” and “Majama.” India didn’t just teach me independence — it cracked me open creatively. It showed me how improvisation is its own kind of discipline, how getting lost is a form of education.

I never imagined I’d be invited back years later to collaborate with artists I once watched on café computers — working with actors like SRK, making videos like “Lean On” that crossed billions of views, nearly dying during spiritual side quests in Leh and Varanasi, falling for Bollywood sweethearts, and still believing every strange turn meant something.

Twenty-five years later I returned to these roads, riding nine hours a day across the Himalayas on a much newer Enfield. And then — perfectly — I ended up performing at a massive Enfield festival in Goa and celebrating afterward in a motorcycle garage, as if time folded back on itself.

Two decades have changed India and me both. But every time I come back, I feel the same truth: growth happens when you surrender to the unknown, when the road teaches you more than any classroom could.

India was my beginning. And somehow, it still is.

[BCCI Women @BCCIWomen]

𝐂.𝐇.𝐀.𝐌.𝐏.𝐈.𝐎.𝐍.𝐒 🏆

Congratulations to #TeamIndia on winning their maiden ICC Women's Cricket World Cup 🇮🇳

Take. A. Bow 🙌

#WomenInBlue | #CWC25 | #Final | #INDvSA

[Golden State Warriors @warriors]

Season 17.

[The Race @wearetherace]

High-tech weather radar? ❌
Stick hand out to feel rain? ✅