DeepSeek V3

General
Release date	2024
Author	DeepSeek
Website	DeepSeek Models
Repository	https://github.com/deepseek-ai
Type	MoE (Mixture of Experts) Language Models

The DeepSeek V3 model represents our most advanced AI architecture, designed for complex reasoning tasks and code generation. With enhanced context handling and improved instruction following, this model excels in technical applications and enterprise deployments.

Key Features

DeepSeek-V3: 671B parameters (37B activated per token), optimized for math, code, and multilingual tasks.
Code Generation: Supports 12+ programming languages
Advanced Reasoning: Chain-of-thought capabilities for multi-step problems
Enterprise-Grade Security: Built-in content filtering and compliance features
Speed: 3x faster generation than previous versions (60 TPS)
Open-Source: FP8/BF16 weights available on Hugging Face

Usefull Links

👉 Local Deployment Guide for DeepSeek V3 👉 Model Weights on Hugging Face 👉 API Documentation 👉 Deepseek V3 Paper 👉 Performance Highlights

AgentRush — Blast Radius Zero

AgentRush is a sandboxed execution agent built for the Vultr Agent Rush hackathon. A user submits a task in the web UI; an LLM on Vultr Serverless Inference decomposes it into steps; URL fetches run on the orchestrator host with SSRF-pinned DNS; generated code executes inside a fresh Docker container per task: no network, read-only root fs, tmpfs workdir, capped memory/CPU/pids, all capabilities dropped, empty environment, wall-time kill, destroyed after the run. Every run returns a verifiable JSON record with per-step commands and sha256 of outputs. The demo video shows six live attacks — rm -rf, infinite loop, fork storm, env scrape, outbound curl, prompt injection — each dying at its intended boundary. Live public deployment on a Vultr VM (fra).

SRE Incident Agent — Blast Radius Zero

The problem. An on-call SRE facing a cryptic 500-line log spends 20–40 minutes of manual grep/correlate work to know what broke and what to do first. LLM assistants can draft guesses, but executing LLM-generated code on a real system is the classic blast-radius problem: run it blind, with network access and secrets in the environment, and a hallucinated diagnostic becomes an incident itself. What we built. SRE Incident Agent closes the loop safely. Paste a log — the LLM plans a diagnostic step, the step runs as real Python in an isolated Docker container, the verified stdout and exit code feed back into the agent's context, and after ≤5 steps it returns a triage report: findings backed by numbers from executed code, three ranked root-cause hypotheses, and an ordered runbook. Nothing the LLM claims is trusted unless an executed snippet printed it. Blast radius zero. The LLM-written code is the attack surface, closed in layers: a static guard refuses subprocess, sockets, URL fetches, file writes, eval/exec and host paths before anything runs (shown as BLOCKED · contained); each snippet runs in a one-shot container with --network none, 256 MB RAM, 1 CPU, 64 pids, read-only rootfs, uid nobody, no-new-privileges — destroyed after every run; secrets never enter the container; a hard 20 s kill per step. The video shows a live containment moment: a hallucinated exfiltration snippet blocked pre-execution. On Vultr. The full backend runs on a Vultr Compute VM (Amsterdam): FastAPI, the Docker sandbox runner, Caddy/TLS. All LLM calls go to Vultr Serverless Inference — primary deepseek-v4.1-flash-1017, whole-step fallback retries on mimo-v2.6-flash. Business case. The author supports storage systems and virtualization fleets already off vendor support, where every incident is triaged manually: this cuts incident triage from ~30 min to ~60 s, with evidence attached.

FlowMedic

FlowMedic is an AI-powered self-healing workflow agent designed to make workflow automation more reliable, intelligent, and resilient. Built to autonomously detect, diagnose, test, and repair broken n8n workflows, FlowMedic reduces the need for manual debugging and minimizes workflow downtime. When a workflow fails, FlowMedic analyzes the execution errors, identifies the underlying cause, and determines an appropriate fix. Instead of simply suggesting solutions, the agent attempts to implement repairs and validates them through testing in an isolated Vultr sandbox environment. This sandbox provides a controlled space to experiment with potential fixes before applying changes to the actual workflow. By combining AI-driven reasoning, automated debugging, workflow analysis, and isolated testing, FlowMedic transforms traditional workflow maintenance into an intelligent, proactive process. Our vision is to move beyond automation that simply executes predefined tasks toward automation that can understand failures, learn from errors, and recover independently. FlowMedic aims to help developers, businesses, and automation teams build more dependable systems while saving valuable time, reducing operational overhead, and ensuring that critical workflows keep running smoothly. FlowMedic: When workflows break, AI brings them back to life.

Forma: AI-Powered Virtual Fitting

Forma is an advanced, end-to-end AI-powered virtual fitting and commerce intelligence system engineered to eliminate sizing uncertainty and high return rates in digital retail. By fusing cutting-edge computer vision, generative 3D avatars, and physical cloth-simulation AI, the platform enables shoppers to generate accurate body models and instantly preview how different fabrics, cuts, and layers drape and stretch in real time. Beyond the consumer experience, Forma operates as a robust commerce intelligence suite that cross-references garment specifications with body metrics to deliver hyper-precise size recommendations and actionable analytics for merchants. By aggregating behavioral feedback and trial data, the system flags manufacturing inconsistencies, predicts demand trends, and drastically cuts down expensive reverse logistics, bridging the gap between digital convenience and physical confidence.

RepeatFix

Airlines lose aircraft availability when a fix doesn't hold and the same fault comes back. The evidence is already in the maintenance logs, but it sits in messy free text that nobody has time to read. In 180,012 real FAA Service Difficulty Reports (2024 to 2026) we found that at least 13% of fixes didn't stick: in 5,010 of 38,937 system fixes, the same problem came back on the same aircraft within 90 days. RepeatFix is a reliability-engineering tool for that problem. It reads raw logbook entries (FAA SDR and NASA ASRS reports) and turns them into structured job cards with an ATA chapter, a component, a failure mode and the action taken. A deterministic repeat engine then ranks the "bad actors": 16,473 repeat groups in the full set, such as one A320neo (N926NK) with 17 cabin fume events. When a new fault comes in, RepeatFix shows the most similar past fixes, the likely causes, the matching procedure from FAA AC 43.13-1B or the AMT Handbooks (FAA-H-8083-30B/31B/32B) cited by chapter and page, and a draft work order. A supervisor edits and approves it; nothing runs on its own. All model calls run DeepSeek on AMD GPUs via AMD Token Factory. The backend is FastAPI on Railway (extraction, TF-IDF retrieval, repeat engine) and the frontend is Next.js on Vercel. Measured: 68% agreement with a keyword parser's labels (not hand labels), a 15.9 s median answer and 20 tokens/s.

Content Co-Pilot

Short-form creators on YouTube Shorts, Instagram Reels, and TikTok lose up to 70% of their audience in the first three seconds due to easily avoidable mistakes: text hidden behind platform UI buttons, poor mobile font contrast, slow scene cuts, and weak curiosity hooks. Currently, creators only discover these algorithmic bottlenecks after publishing, resulting in dead reach and wasted production budgets. Creator Co-Pilot AI is an automated, pre-publish diagnostic engine built to solve this. Creators upload their draft video to the platform, which extracts a 12-frame scene-aware contact sheet and audio dynamics in under 5 seconds. An XGBoost and SHAP diagnostic model scores the video against category-specific viral benchmarks to identify exact pacing deficits. Finally, the platform deploys open-source multimodal agents hosted on AMD Instinct hardware to deliver timestamped edits, UI safe-zone audits, and data-grounded script rewrites. By turning algorithmic guesswork into explainable metrics, Creator Co-Pilot AI saves creative hours and maximizes viewer retention.