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Dion Almaer

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Keeping your A-Team together with developer AI

September 30, 2024

When I look back at my career, the most fun and fulfilling times I have had has been tied to impactful projects with a team that is clicking. This has often happened at a startup, but it has also happened in some magical moments where the team is able to move fast and with agency within a larger company.

How often have you heard, or thought, the following?:

Remember when we were small? We moved so fast back then.

What if you could stay small and nimble, but get so much more done?

One core philosophy that DHH has often discussed as a key part of The Rails Way, is how it scales from a single developer. From Hello World to IPO. The way to do that is to be able to do more with less, and keep as much of the system in your head as possible. Allow room for the important pieces, and keep complexity from taking up valuable space.

When we come up with new infrastructure that compresses the complexity, we see amazing scale such as what the WhatsApp team was able to build with a small team.

I believe AI native tools can help here in a slightly different way. For example, you can somewhat outsource some of the complexity to the system. The brain budget can be augmented, and some of it can be swapped in, in real time.

This is an area of AI that I am particularly excited about, and I am seeing it occur in practice every day with customers at Augment.

When talking to one customer that is startup sized, they said:

We are growing like a weed, and I was nervous that we would have to grow the team… which scared me. I love our tight-knit crew and how we have trust and minimal coordination issues. But since using Augment and other AI tools we are finding that our work isn’t scaling linearly… we are so productive that we don’t have to grow to meet feature demand. I hope this lasts as long as possible!

This resonates a lot! I have seen the coordination headwinds first hand, and anything that you can do to minimize them will result in HUGE productivity gains… and will also give you more joyful moments.

How are AI tools helping?

I think that the following properties are compounding:

More than “faster typing”

It’s easy to think about features such as code completions as a way to speed up typing. Speeding up typing is just the start. The next step is taking care of raw toil and tedium, but where savings really kick in are when the suggestion brings you something that you maybe didn’t necessarily know what to write. I love it when this happens, especially when it teaches me something new about the codebase or another way to do something.

No more “Reading the docs”

The LLMs have read the docs for you, and much beyond. They have read your code, and that of all of your dependencies. They may have consumed knowledge from other sources (Linear tickets? Slack channels? PRs with comments?).

Instead of hunting down documentation, you can use features such as Chat to ask questions that map to your exact task at hand. You can personalize responses (maybe you want a terse reply, or the opposite?). And having help that maps to your context means you aren’t translating between the examples that happen to be in the docs.

Saving time not just for myself, but for my team

I hate interrupting my coworkers when I am stuck. Now the first line of defense allows me to stay unblocked by working with Augment. This can save a ton of “clock time” when my coworkers are busy… or on the other side of the world!

This doesn’t mean I don’t want time with my colleagues, but it can be focused on working together on more novel and creative problems.

Confidence working across unfamiliar codebases

Maybe you aren’t as experience in Rust and have been nervous to touch that part of the codebase. You don’t have to worry as much about the idioms of the language, and you can use these tools to help you learn as you use autocomplete functionality and chat to act more declaratively.

This flexibility is being noticed, and “full stack” is morphing into the rise of the “product engineer”:

Many argue that front-end engineering is fading due to AI tools, but I see a convergence of roles.

Front-end devs can now generate schemas with tools like @supabase’s https://t.co/ZWMGf6cVj5, while back-end devs can scaffold UIs with @vercel’s @v0.

This is the rise of the…

— Kenneth Auchenberg 🛠 (@auchenberg) September 18, 2024

This also works when your team has to interact with another team at a larger company. You may not have to wait for the work to be done by them, and instead can dive in and collaborate to get something done!

From code completion to task completion

Code completions are still a favorite feature. I feel like I am dancing with my AI partner and quickly iterate and steer. But we are now seeing the ability to share your intent at a higher level, and have new UX that will quickly help you get a full task done. I’m very excited to share what Augment has been doing here.

Think you can keep your A-Team?

Now, I may be biased… but I think the best way to keep the A-Team together is to have a developer AI platform that has the deep codebase and external context awareness to act like you are working with the experience of your entire team vs. a knowledgable engineer that knows the basics. The difference is night and day, and I get very happy thinking about smaller teams with super powers. I hope you do too!

And maybe you will have the type of outsized impact that 13 employees did at Instagram, or 55 at WhatsApp, or 50 at Mojang (Minecraft), or if you are truly lucky… Donald Knuth with TeX?

(I was thinking about TeX and Professor Knuth again when Matt Holden recently shipped TexSandbox, a tool I wish I had in my Math courses at Uni!)

Introducing Augment: a company dedicated to empowering developers with AI

April 24, 2024

I’m incredibly excited to share that Augment, the company I joined to help empower developers, has come out of stealth.

With a lot of FUD around AI taking all of the knowledge worker jobs, including those of developers, I believe it is important to get across the counter argument:

“Don’t fire Kevin for Devin just yet. Augment Kevin with super powers!”

Me

If you think about what software engineers actually do and what AI excels at, you should reach the same conclusion. It’s easy to anthropomorphize AI tools, especially when you’re chatting with them and considering their portrayal in science fiction. With that in mind, I believe in creating systems that resemble J.A.R.V.I.S more than HAL.

As we develop these systems, it’s essential to remember that humans and computers have unique strengths. The real magic happens when humans take charge, supported by ever-present, fully connected computer systems.

By doing so, we can not only improve life for developers individually, but also empower teams and organizations to accomplish much more with reduced toil and communication costs.

I’m passionate at doing my part to help here, and I want to share my journey to Augment with you.

Seeing the future of software development

I love programming. Whenever I write some code, it tends to be a good day. There is something about the creative process that ends with something tangible that is good for my brain. Any platforms, tools, or services that allow me to stay in that certain flow of development become favorites. There is an art to taking an idea, breaking it down, and making progress.

The longer I am on the path to running code that works – or getting effective help back onto the path when it isn’t working – the better I feel.

On the flip side, whenever I am doing something that feels like toil, or I feel really stuck, the worse I feel.

There have been a couple of times when I saw how AI technology could dramatically help:

  • I worked with a research team inside X at Google who built models (in the pre-LLM/transformer days) that could help the highly skilled SWEs keep up with the constantly evolving monorepo. This was often very boring work, ripe for a computer to help.
  • I worked on a project at Shopify that uses LLMs to bridge the complexity of GraphQL for developers wanting to integrate with merchant data. This quickly taught me lessons, such as:
    • It’s easy to show a cool (somewhat contrived) demo
    • It’s hard to build something great that works at scale in the real world
    • One LLM isn’t the answer for all use cases
    • It’s not just quantity… quality data matters
    • Having a system that can really do well wrt evaluations is vital as you iterate

Projects like these gave me the evidence to see how software engineering is going to radically change in the future, and pairing AI technology with developers will be the driver.

Meeting the Augment team

I was sold on the opportunity that this AI wave could allow us to help developers in new expansive ways. I started to explore, and this exploration lead me to chatting with a couple old friends, Luke Wroblewski and Sam Pullara who are building companies at Sutter Hill Ventures, a pretty unique VC firm.

Luke and Sam grinned as I spoke about my desire to build for developers with AI, and quickly introduced me to the founders and team behind Augment.

I met Guy Gur-Ari, the co-founder leading the research efforts at Augment. He had already assembled a team of AI researchers and engineers who had many years of expertise with ML and how it can be applied to code. This was important to me, as I had found that to build something truly great, you need the ability to make changes across the entire stack. You want to be able to change the engine along with the other parts of the car!

Igor Ostrovsky, the other co-founder and pioneer of Augment, also gave me a lot of faith that we had the broad technical expertise to pull this off at scale. His proven track record with distributed systems as Chief Architect of Pure Storage, developer focused work at Microsoft, and his deep dive into AI as an entrepreneur in residence with SHV was inspiring.

Then I discovered that Scott Dietzen had joined as CEO. I first met Scott at the birth of enterprise Java, where he was CTO at BEA WebLogic, my favorite app server of choice.

As I met the broader team, I had a strong feeling that this was a team with the focus, experience, and skill to take a shot at building the best AI platform and ecosystem for developers.

The team had gone deep in building foundational technology that is needed to solve the meaty problems that developers have, especially at scale. These include building a system that:

Has an expert understanding of large codebases

There are solutions out there that feel like you have access to a system aware of core technology. They have a solid understanding of programming languages, and popular frameworks.

When using Augment, we want you to feel like you are working with the joint intuition of your most seasoned engineers at the company, and those with deep expertise on the dependencies that you use.

Any suggestions need to reflect the APIs and coding patterns in your company’s code so your team can use it on your actual day-to-day work.

Produces running code

The custom AI models and infrastructure are tuned for code and coding use cases avoiding frustrating hallucinations and focuses on improving code quality… not just productivity.

Operates at the speed of thought

There were many search engines before Google, but I remember trying it for the first time, and seeing how the experience was a step change. The quality of the results were next level AND the speed to return them felt different.

Working with LLMs can be a lil… slow, which massively degrades the experience and can keep knocking you out of flow.

The team had built a fast inference — 3x faster than competitors — built on state-of-the-art techniques, including custom GPU kernels, and I felt the difference in the experience.

Supports multiple developers & teams

Software development is a team sport. There are so many areas where technology can help scale and improve the use of best practices across a team, help you learn a complex codebase, and get new engineers onboarded faster.

The scale of computers allow a system to attend to do much more, and they are available 24×7.

I have learned the power of small teams. We have seen with early customers that the shape of teams can change when you deliver the right capabilities. If we can enable smaller teams to do more, and for teams to do more in parallel, we result in better software and happier devs to boot!

Includes strong IP protections

Your company’s source code is precious. Augment was designed from the first line of code for tenant isolation, with an architecture built to protect your IP.

Try Augment

Joining Augment has already been a blast. Moving at startup speed with a great crew all focused on helping developers is a dream come true for me. I feel very fortunate to have the opportunity to go after this problem space with a small (but growing! Join us?) team.

We are heads down delivering on our promise, working closely with early access customers, who have been a key part of our product development thanks to their fantastic feedback (thank you!).

We are furiously working our way to a public product launch that we can’t wait to share.

Until then, if you are interested in kicking the tires early, please sign up for the waitlist!

Building AI Dance Partners (and your role as a good lead!)

December 31, 2023

tl;dr LLMs give computers new abilities to be better partners for us humans, and if we build the right systems we can transform how we work together. I have learned some lessons on the building side, but also on how to do more as an augmented human to get the most out of this new world!


A dream stirred me from my sleep. I found myself on the set of ‘Dancing with the Stars,’ but with a twist: my partner was not human, but a robot. As I lay there, half-awake at 3am, I pondered the meaning of this mechanical ballroom dance. Then it clicked… it was a metaphor for the work I’ve been deeply immersed in at the close of 2023: creating computer systems that augment human capabilities, giving developers and their teams superpowers in software delivery.

The Dance

I’ve always believed in the power of combining the best of both worlds: human creativity and computer precision. The best user experiences have always weaved brain and tool, these days including those that are digital.

LLMs have changed the game in that precision has a brand new capability: a new layer of intuition that we can tie to. A way to combine my Systems 1 and 2 brain with a mesh of combined thought. Back in the dream, my subconscious was painting a picture of the ideal partnership where the human mostly leads, and the machine follows in a tightly choreographed back-and-forth. Just like picking up a tool such as PhotoShop, it can still take time to master the steps, and the dance changes as the capabilities change. How can we best use the strengths and weaknesses of each partner so that they work as one?

Crafting the Perfect Partner

I’m currently iterating on a dancer that developers can shape into the best partner possible. Speed and skill are crucial. A slow computer is like a dance partner with two left feet, disrupting the flow and making collaboration frustrating. Skill, on the other hand, is about quality and finesse—leading without stepping on each other’s toes, sharing knowledge to maintain the rhythm.

The Car and the Engine

I was excited to join a Sutter Hill Ventures startup for many reasons, and my expectations have been very much exceeded. Not only do we have a solid financial backing that allows us to really focus on building a game changing product and business, but the support that the Sutter Hill team has is special. I get to work with my favorite UX person there is. The enterprise sales playbook is ready to run. And on and on.

The team itself (founders, CEO, and everyone else!) is not only world class, but there is a strategic bet that I strongly believe in for building the absolutely best product. The heart of the team has AI researches who deeply understand every part of the stack.

It’s one thing to build a car using someone else’s engine; it’s another to be able to fully tinker with that engine or even build your own.

In 2023 we have learned so much as a community. First we had the transformational moment when developers got to poke at what could be done with OpenAI APIs (and then so many more). There was the prompt engineering, RAG’ing, and pushing the boundaries of what’s possible.

Embracing Constant Change

The model tier is just the beginning, and going from demo to a production system requires a world of work to be done around it.

New models and research are popping up on a daily basis, so how do you filter out what could be helpful? How do you determine its utility for your specific needs? How do you ensure your data is accurate and current? Are your evaluations truly reflective of quality, or are you just fitting the last piece of a puzzle?

Metrics

Measuring what matters here is hard. For example, with coding tools, I often see discussion around the amount of codethat is created, or the Completion Acceptance Rate, but when you watch this play out in practice with your users you realize…. wait a minute…

Do we want to always be creating code if it’s adding entropy into the system? If that code is iffy, and if the human can’t tell, then maybe we are adding problems. And, wouldn’t it be nice if we maybe could… delete code and simplify?

For completion acceptance, I can get very different results by changing the system to vary the amount of code that comes back, or the latency, and many of the habits that you build with the developers. The habits have been really fun to watch. Seeing cohorts that start by waiting for the system to do things vs. communicating more and moving quickly.

And when I do side by side comparisons, I see the huge difference where one system can have a hire acceptance rate that ends up with code that doesn’t run. Don’t I really want to be tracking time to running code that is high quality?

Here’s to 2024

We are somewhere in the journey that is akin to constant improvements that we can see with other tools such as Midjourney.

I’m grateful for my team’s collective ability to build everything needed for the ultimate coding dance partner. We are building the platform that enables the building of this partner, to iterate on it, to take in the innovation from open source and our own research, and man I’m having a great time doing it.

I can’t wait to share it with more of you. If you’re a developer who spends most of your day coding, enjoys giving feedback the moulds a product, and are interested in getting early access, I’d love to hear from you.

Happy New Year, and may this become true!

Prediction: 2024 will feel like a breakthrough year in terms of AI capability, safety, and general positivity about its potential impact. In the longer term, it'll look like just one more year on an exponential that can make everyone's lives better than anyone's today.

— Greg Brockman (@gdb) December 31, 2023

NOTE: Of course, this article was written by both Dion Almaer and the dancer within Type.

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The right thing to do, is the right thing to do.

The right thing to do, is the right thing to do.

Dion Almaer

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