@daytonaio AI Builders meetup hits NYC 🗽 on June 23 powered by @Oracle & @Datadog. Talks from @daytonaio, @Oracle, @datadogdevs, @useblacksmith, and @AnchorBrowser: agent-driven QA, cloud architecture for AI workloads, how AI is reshaping CI, and more. We'll close out the night with networking, pizza & beverages. Spots are filling fast, be sure to RSVP in time ⬇️ luma.com/nyc-ai-builders-01

See what your AI coding agent is doing with Datadog Lapdog... by Chris Ebert 👉🏻 chrisebert.net/see-what-your… @datadoghq

Feature flags are becoming part of the AI engineering workflow. With @datadoghq's Feature Flagging MCP tools, agents can: → Create feature flags → Check implementations in code → Inspect flag configurations → Start canary rollouts From validating fallback values to updating targeting rules, your agent can help manage feature releases without leaving the editor. Watch the demo ↓

LAUNCHED: Two new ways to run experiments in Datadog 🧪 ✅ Fixed Sample Experiments are now available, giving you another analysis method to choose from during experiment setup. ✅ Bring Your Own Flag (BYOF) is now generally available, making it easier to connect experiments to your existing flag infrastructure. Whether you're looking for more flexibility in how you analyze experiments or want to experiment on top of your existing flag infrastructure, both are now available in @datadoghq.

Thinking about consolidating your feature flag tooling? @datadoghq's feature flag migration CLI helps you migrate flags, map environments, validate targeting, compare evaluations side-by-side, and export migration reports so you can verify everything works before cutover. See how it works ↓

We had a packed room for the Product Analytics session at DASH 👏🏻 Developers aren't just shipping features anymore, they're increasingly expected to understand how those features get adopted, where users get stuck, and what drives activation. The interest in Product Analytics this week was a good reminder that building the product and understanding its impact are becoming part of the same workflow. @datadoghq

"We rewrote our entire database system from Python to Rust." - @thsottiaux, @OpenAI AI is making large-scale rewrites and modernization projects economically viable again. That's the pro. And yes there are some cons sometimes - "One bad example is when it deleted an entire research cluster." The conversation is no longer: "Can AI write code?" it's "How much autonomy are you willing to give it?" The more agents move from generating code to operating systems, the more important it becomes to understand: → What they did → Why they did it → Which tools they used → What caused failures That's why observability is becoming a core part of the AI stack. @datadoghq

"The biggest breakthroughs in AI coding aren't coming from bigger datasets, they're coming from feedback loops" - @_sholtodouglas of @AnthropicAI Sholto described two broad ways models learn: → Pretraining (learn from existing data) → Reinforcement learning (try things, get feedback, improve) Coding is particularly suited to the second approach, and that's why coding capabilities are improving so quickly. You can measure success: Did the code compile? Did the tests pass? Did the task get completed? The model gets a signal and improves. Sholto pointed to @cognition's latest FABLE evaluation, where Claude Opus 4 was reported to be 2–3x better than competing models at producing mergeable, high-quality commits on difficult coding tasks. How often does a human need to step in? Sholto said his personal expierence: ~30 second interventions → then ~3 minute interventions → now somewhere between 30 minutes and 3 hours That's a massive change in a very short period of time. A better way to think about progress might be: How long can the model work autonomously before you need to take over? #fable #anthropic @datadoghq @atalwalkar

We build with you - CEO, @oliveur, presented the scale at which we build with this one slide during @datadoghq DASH 📈

CPO’s unite 🤝Datadog’s @ybhighheels and @vercel's @tomocchino at @datadoghq DASH

Now this is how you do a CEO panel 📸 @nerdsane (VP of AI @ @datadoghq) and Clémence J Burnichon (Sr Director Eng @ Datadog) took the stage with some of the best CEO’s in tech right now: @zachlloydtweets - @warpdotdev @dakshgup - @greptile @jayair - @opencode

Developers developers developers @dakshgup @jayair @nerdsane @zachlloydtweets

A great day to talk about the new Fable mythos launch w/ @atalwalkar @emollick @_sholtodouglas

Our very own @atalwalkar sat down with @AnthropicAI's @_sholtodouglas to chat everything AI at DASH - from the release of Fable to deep reasoning, clips incoming… 🔥

“Fable is 2-3x better than any other model at producing mergeable, high-quality code” (according to a Cognition eval) - @_sholtodouglas speaking with @atalwalkar at DASH today. Dang!

A meeting of CEO’s and friends - @datadoghq's Olivier Pomel and @huggingface’s @ClemDelangue at DASH