AI Architect & Builder - Inference, LLMs, SLMs GSoC Org Admin - Jenkins | Docker Captain | CNCF Ambassador
New Delhi
Joined December 2018
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- Following1.3K
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Shivay Lamba retweeted
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
Shivay Lamba retweeted
A lot of people trying to imply that last 1 year of vibe coding is responsible for all the security vulnerabilities dropping across every piece of software this week.
But if you take a deeper look you’ll see most of the CVEs are impacting actually hand written code from the past; and in fact AI agents are super important to defend against cyber attacks (quickly deploying changes at scale, monitoring situations, writing the code to patch the exploits etc).
Just because humans wrote it doesn’t mean it is necessarily more secure than what AI wrote.
Shivay Lamba retweeted
We’ve spent the past year developing new primitives for GPUs. We believe this enables new algorithms to change how machines learn, open new markets, and dramatically reshape AI’s cost structure.
We have raised a $1 billion Series A, co-led by Index and Lux.
Shivay Lamba retweeted
Had a wonderful time presenting 2 talks at @AllThingsOpen:
1. How do you secure coding agents using @Docker Sandboxes
2. LLM inference on edge using @PyTorch Executorch and LiteRT for @Qualcomm_Dev QNN
Always have wonderful discussions and cross sharing of feedback at ATO!
Also great to catch-up with some of the best folks in the business after such a long time:
@kentcdodds @ossia @fharper @SwiftAlphaOne
This is only possible at ATO 🫡
Had a wonderful time presenting 2 talks at @AllThingsOpen:
1. How do you secure coding agents using @Docker Sandboxes
2. LLM inference on edge using @PyTorch Executorch and LiteRT for @Qualcomm_Dev QNN
Always have wonderful discussions and cross sharing of feedback at ATO!
Shivay Lamba retweeted
New research: We developed JOLT, a post-training method where one model acts as both teacher and student, with the teacher trained to give feedback the student can use.
On MATH, it matched GRPO's accuracy with 6.5x fewer training tokens.
perplexity.ai/hub/blog/a-goo…
Shivay Lamba retweeted
Today we release mammoth-1 and elephant-1!
We post-trained mammoth-1, the #1 ranked best decision model in the world, and elephant-1, which is the best model under 15B (and beats Jev).
We're open-sourcing all model weights and provide a technical report detailing all our training and data methods.
I built Kubetrace - using Beam with @nebiustf
xitter.cf/HowDevelop/status/2111…
An AI coding agent becomes useful when it can investigate a problem, change the code, test the fix, and follow through to a working deployment.
That’s what I wanted to explore with the newly launched @reflection_ai Beam 🚀
Beam is built for coding, reasoning and agentic workloads. KubeTrace AI puts those capabilities to work on @kubernetesio incidents, with inference through @nebiustf.
The agent can:
→ Investigate logs, events and application code
→ Trace a failure to its cause
→ Propose code or Kubernetes manifest changes
→ Run regression tests in a sandbox
→ Build and deploy the repaired application
→ Check whether it recovered
The execution layer checks every proposed change against policy, and hidden checks decide whether the incident is resolved. You can inspect the evidence, patches, test output and failed attempts.