Slack: Sourcegraph serves nine of top ten public tech companies
“We have like nine of the 10 top Public tech companies as customers and like four of the six top banks and like Uber and Stripe and so on, all these companies using Sourcegraph for code search.”
Emberikos: Sourcegraph's AMP punches above its weight and initiated agent standards
“Their product has a great reputation of just being, like, you know, punching way above its weight, but I think the other thing that I really respect is that they helped initiate this whole, like, standardization around, like, agents.md and, like, dot agents sl…”
Slack: Amp is growing more than 50% month-over-month
“It's growing more than 50% month over month, a lot faster in, you know, some weeks.”
Slack: Two-to-three person teams reach six-figure run rates on Amp
“So we have teams with like two or three people that are on annual run rates of like hundreds of thousands of dollars.”
Slack: Sourcegraph's Amp has positive gross margins
“It just so happens that we are positive gross margins along the way.”
Slack: Sourcegraph skips months-long compliance checklists for Amp using existing trust
“So there's a lot of things on AMP that we do, like no consistent pricing, no user model choice, no checking off all the boxes that security and compliance and legal want that, you know, takes nine months. We're able to get away without doing that stuff because…”
Ball: Amp core team has 8 people, ships 15 times daily without code reviews
“I think we're around eight people now on the AMP core team, and we still don't do formal code reviews. We still push to main. We still ship 15 times every day.”
Ball: Sourcegraph internal usage split 50/50 between CLI and VS Code
“We had, like, an internal poll last week at our company meetup where Beyang was asking who of you uses the CLI and who of you uses VS Code, and it was a fifty-fifty split”
Slack: Competitors discounted products up to 100% to win deals against Amp
“So we've had one head to head loss with Amp where we lost against the usual players. And the reason why is one of them discounted their other product a hundred percent for two years. The other one discounted at 85% for two years, which is just crazy.”
Ball: Unify and make logs verbose for AI agents, not human readers
“Instead of having the client log and having the browser log and having the database log, let's have one unified log, because then it's easier for the agent to just look at this log and make sense of it. And then it turns out it doesn't have to be nicely format…”
Ball: Developers are actively modifying codebases to suit AI agents
“What we're seeing now with agents is as soon as somebody has seen what it can do, they have such a multiplying effect or this brings so much value that people are willing to adopt the code base for this. Like the first time in how many decades where people are…”
Slack: Non-coding board member used Amp to replace $250,000 enterprise software
“And we've had non coders, including one of our board members who built something with AMP that replaced like 250 K a year piece of software that he used for a lot of their internal fund tracking.”
Ball: The Sourcegraph Amp team does not use formal evals
“I think we don't have any set evals. We don't. And this was controversial up until a week ago, I think, when I think Boris from
Or two weeks ago from Anthropix that they don't have evals for the coding agent too.
But we don't, and we haven't had them.”
Liu: Developers spend most time reading existing code, not writing new features
“I think we both realized that we're spending a lot of our time in the process of reading and understanding the existing code rather than building new features. Cause all that is a prerequisite for Being able to build quickly and efficiently.”
Liu: Data quality and structuring pipelines outweigh model architecture
“Your model architecture can only go so far if your data is, is garbage. So you really need a high quality data pipeline, and that means not only having, you know, in our domain, high quality code that can serve as the underlying data to use, but also a way to …”
Beyang Liu: Simpler baseline systems often match or beat fancy AI models
“Doing the simple thing, it establishes a baseline. Like oftentimes you'll find that like the doing the fancier thing is often sexier. And it's certainly these days it's like trendier, right? Cause you can kind of claim the mantle of like, ah, you know, I made …”
Liu: Sourcegraph Cody uses StarCoder 7B, matching larger models with context
“The primary model that Cody uses for inline completions right now is StarCoder seven billion. And with the benefit of context that actually matches the performance of you know, larger proprietary models.”
Liu: Single Developers Will Build Apps That Currently Require Large Teams
“If your goal is to build software as it exists today, then yes, I think in the future, a single human will be able to build applications that today require large numbers of people to coordinate.”
Liu: Software Teams Will Remain Large as Scope and Ambition Expand
“And so I still think we will have large teams of software developers in the future. They will just You know, each individual will be responsible for far more feature scope than they are today, and the system as a whole will be more sophisticated and more power…”
Liu: No AI coding tool uses one model for completion and chat
“Everyone uses a range of model. No, like, no one uses the same model for, like, inline completion versus, like, chat because the latency requirements were.”
Liu: Cody matches GitHub Copilot completion acceptance rates using open-source StarCoder
“Like today, Cody uses StarCoder for inline completions, and with the benefit of the context that we provide, we actually show, like, comparable completion acceptance rate metrics. It's kind of like the standard metric that folks use to evaluate inline completi…”
Yegge: AI coding will fragment into many specialized, fine-tuned models
“And that, that fragmentation of models actually, we expected to continue and proliferate, right? Because we are fundamentally, we're a recommender engine right now. We're recommending code to the LLM. We're saying, may I interest you in this code right here so…”
Liu: Long-context recall depends directly on needle-in-haystack training loss
“The skill with which models are able to take advantage of context is always going to be dependent on how that factors into the impact on the training loss, right? So like, If you want long context window models to work well, then you have to have a ton of data…”
Liu: Pure transformer models are insufficient to support autonomous AI agents
“We're actually a little bit, I think, more bearish than the average, you know, AI hypefluencer out there on the feasibility of agents with purely kind of like transformer-based models.”
Yegge: Sourcegraph avoids autonomous AI agents until someone builds one that works
“We're not going in the agent direction, right? I mean, I'll believe in agents when somebody shows me one that works.”
Liu: AI coding assistants must pull context beyond Git repositories to succeed
“And I don't think the AI developer will be any different. It will need to pull context from all these different sources.”
Liu: Synthetic data and task-specific fine-tuning provide alpha for code automation
“I feel like most models today, they still use, like, combination of, like, the stack and the pile as, like their training corpus but you can only stretch that so far. At some point, we need more data and I don't know. I think there's still more alpha in, like,…”
Liu: Codebase complexity is software's bottleneck, not code generation speed
“The real problem of modern software development, I think is, is not how quickly can you write more lines of code. It's really about managing the emergent complexity of code bases as they evolve and grow, and how to get, how to make like efficient development t…”
Byung Liu: Sourcegraph's Cody is the only AI fetching full codebase context
“As far as I know, Cody is, is the only AI enabled editor assistant or coding tool today that, that fetches context as kind of like as broadly as we do.”
Byung Liu: Sourcegraph's code graph achieves compiler-level accuracy across languages
“Like that's the code graph that we spent the past 10 years building. And we can do that essentially in any language, any code base at compiler level accuracy.”
Byung Liu: Sourcegraph will offer a self-hostable LLM within six months
“And while we don't have one that's like completely self-hostable yet, I think, you know, with all the interesting things happening around, you know, llama, alpaca and that sort of thing, I think like within six months we'll, we'll have like a viable language m…”