Caleb Elston

Building something new. Co-founder & Former CEO of @delighted (Acquired by @qualtrics)

[Caleb Elston @calebelston]

After 10 years of building @delighted, @madeofcode and I have decided it’s time to pass the baton to @cstudt as we’ll be stepping down to create space for our next adventure. I want to thank our entire team, our customers, and everyone who supported us along the way!

[Caleb Elston @calebelston]

@jgreze @TownAI @arampell @a16z @kirstengreen @ForerunnerVC @firstround @altcap @conviction Congrats JDG and team, looks incredible.

[Caleb Elston @calebelston]

@dyett Congratulations James!

[Caleb Elston @calebelston]

Worth a watch, like a public YC Office Hours https://t.co/ZPsLBh922N

[Caleb Elston @calebelston]

Fun surprise @sama joined the @stripe sessions chat with @patrickc this afternoon.

[Caleb Elston @calebelston]

@mvanhorn Happy birthday!

[Jason ✨👾SaaStr.Ai✨ Lemkin @jasonlk]

So many mature software companies still treat AI as basically a check-the-box feature

This will lead to their death from uncompetitiveness

I see it already so many places

[Sam Gerstenzang @gerstenzang]

Two new financings, one new company, and a lot of open roles

We’ve been busy at Boulton & Watt. In March, we announced Moxie’s Series C and Meadow’s Series A. We also shared that we started our third company, Goodlane Logistics, in the freight brokerage space.

We’re also trying something new as we build out Boulton & Watt’s recruiting team: an in-person apprenticeship for ambitious early career candidates.

Most companies say talent is important, but few companies actually show that they mean it through their actions. We treat talent as its own deep discipline. At the same time, we’ve also seen that there are few opportunities for junior talent to really develop and grow. That’s why we’re starting a talent apprenticeship, in person, in NYC.

More on this role and others: https://t.co/DwW1eXX81f

[NASA @NASA]

Welcome home Reid, Victor, Christina, and Jeremy! 🫶

The Artemis II astronauts have splashed down at 8:07pm ET (0007 UTC April 11), bringing their historic 10-day mission around the Moon to an end.

[Jeff Weinstein @jeff_weinstein]

Very excited to share (a preview of) @stripe projects:

A new way for you—and your agents—to programmatically provision and manage the internet’s best developer services, right from the CLI.

https://t.co/mOSaKMEqf2 to sign up for early access.

Demo ⤵️

[Jeff Weinstein @jeff_weinstein]

🚧 looking for 3 developers who like to try new tools and give (critical) feedback—this weekend...

we have a new cmd line tool for those building new apps.

if you're willing to write up your thoughts or send a video feedback walking through it, dm or email jweinstein at stripe.

[Sam Gerstenzang @gerstenzang]

Excited to share the news: Meadow has raised $11m to continue serving families across the country. And excited to get to continue to work with our partners, Lachy Groom and the folks at Haystack.

Some quick facts:
* We became the largest independent deathcare provider in California in less than two years
* With our expansion in to Texas and Washington (and Arizona next), we're on track to 3x for the third year in a row
* We've served thousands of families with a NPS of 85
* 1/3 of our customers are preplanning
* We probably have more software engineers on our team than all other funeral homes combined 😂

https://t.co/aiAnRxiJRL

[Sam Gerstenzang @gerstenzang]

Honored to be @danshipper's guest! Check it out here: https://t.co/dMFWTCClCQ

[Caleb Elston @calebelston]

@grinich 🙌

[Andrej Karpathy @karpathy]

It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow.

Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes.

As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now.

It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.

[Rasmus Andersson @rsms]

There used to be two playbooks for commercial software:
a) be first to market
b) make the best product

Being first was rarely important, yet so many software companies operate this way. “We must ship by this time next month or we’ll lose.” A shallow way to build, in my opinion.

Now with AI tools having gone from “lol nice JavaScript try again” in Jan 2025 to “damn, nice C program, take the wheel” in Jan 2026, there’s only one playbook that remains: make the best product. Now anyone can “compete” with you if being first is your differentiator.

So don’t make a hundred products or a hundred features quickly just because you can. Instead leverage this “huge cheap skilled workforce” you now have to build something really good, even if it takes time.

You can’t blame timelines for janky scrolling or broken text editing anymore.

Build something that’s meaningfully different, something that you can be proud of a decade from now

[Caleb Elston @calebelston]

@lalkaka @usefiretiger @Cloudflare @segment @Twitch Congrats on the launch! 🙌

[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.

[Caleb Elston @calebelston]

@borisjabes @ycombinator @census @fivetran Congrats!

[Caleb Elston @calebelston]

@glencoates @OpenAI @tobi @Shopify Congratulations, super exciting!

[Caleb Elston @calebelston]

Well, Netflix became HBO faster than HBO became Netflix.

https://t.co/DLTMQMPhfg