Terracotta vs Llama 2

Terracotta is an Infrastructure as Code governance tool that audits every pull request before merge, checking Terraform and OpenTofu changes against live cloud resources, remote state, and your team's governance policies. It installs as a GitHub or GitLab app, posts findings in the PR thread, and builds a tamper-evident audit trail regulators can export. The product targets platform engineering teams that need security, drift, cost, and compliance checks without rewriting CI pipelines.

Static scanners like Checkov or tfsec lint HCL syntax and known misconfigurations, but they never compare a plan to what is actually running in AWS. Terracotta closes that gap by correlating code, Terraform state, and live resources so drift, cross-PR conflicts, and hidden blast radius show up before anyone clicks merge. Its guardrails are written in plain English rather than Rego or Sentinel, which lowers the bar for teams that lack a dedicated policy-as-code engineer.

DevOps leads and platform engineers at regulated shops use Terracotta to block public S3 buckets, open SSH rules, and unapproved cost spikes at review time instead of in production. Security and compliance teams get a fleet-wide dashboard with drift posture, policy compliance rates, and exportable records for SOC 2 or HIPAA audits. Developers keep working inside GitHub or GitLab because findings arrive as PR comments, not another portal to check.

Beacon, Terracotta's in-PR chat assistant, answers questions about specific findings using context from the repo, plan output, and drift reports. The Platform tier adds unlimited drift repos, IAM and blast-radius analysis, Slack notifications, and a command center dashboard for $49 per engineer per month. Enterprise customers can run Terracotta self-hosted with SSO, SAML, and custom integrations for HCP Terraform or CircleCI.

The next generation of our open source large language model

This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters.

Llama 2 was trained on 40% more data than Llama 1, and has double the context length.

Training Llama-2-chat: Llama 2 is pretrained using publicly available online data. An initial version of Llama-2-chat is then created through the use of supervised fine-tuning. Next, Llama-2-chat is iteratively refined using Reinforcement Learning from Human Feedback (RLHF), which includes rejection sampling and proximal policy optimization (PPO).

Meta and Microsoft have partnered to unveil Llama 2, the open-source successor to their widely-utilized large language model, Llama. This groundbreaking model is designed to enhance the capabilities of AI, offering it free for both research and commercial use. Recognized as the preferred partner, Microsoft is integrating Llama 2 into its Azure AI model catalog, providing developers with robust cloud-native tools and optimization for Windows platforms.

Llama 2 is also accessible through other major providers like AWS and Hugging Face. Dedicated to responsible AI innovation, Meta and Microsoft emphasize transparency and community-oriented development with resources like red-teaming exercises, a transparency schematic, and a responsible use guide. Collaborative initiatives such as the Open Innovation AI Research Community and the Llama Impact Challenge are also part of the rollout, aiming to spur responsible applications of Llama 2 across various sectors.