Vijay Tank — Tech, Code & Game Dev

Overview This guide walks through setting up a self-hosted LiteLLM proxy that unifies multiple free-tier LLM providers (NVIDIA NIM and OpenRouter) behind a single OpenAI-compatible API endpoint. It also covers automated model-availability probing and integrating the resulting gateway with an AI coding CLI. No API keys, account names, personal identifiers, or real secrets are included anywhere in this guide. All example values are placeholders — replace them with your own credentials. Why build this Free-tier LLM providers often enforce low per-minute rate limits (e.g., 20-40 requests/minute). Model catalogs change frequently — models get added, deprecated, or temporarily rate-limited. A single unified gateway lets any OpenAI-compatible client (coding CLIs, IDE extensions, custom scripts) fail over automatically across multiple providers/models without code changes. Part 1: Probing Model Availability Before wiring a provider into a gateway, verify which models are actually ...

OpenAI just dropped GPT-6 Astra on September 3, 2026, and it's already turning heads in the developer community. If you're building AI agents, automating workflows, or running local LLM stacks, here's why Astra matters—and why it might be the better choice over Anthropic's Claude Fable 5.1, especially when you factor in real-world cost and performance. What Is GPT-6 Astra? GPT-6 Astra is OpenAI's first "Critical"-rated model under its Preparedness Framework, meaning it meets the highest cybersecurity capability threshold with stronger safeguards for release. [cite:52][cite:57] It's designed to operate computers, browse the web, work across large file collections, write and execute code, and continue complex tasks with minimal human guidance. [cite:60] Key specs: Context window: 1,050,000 tokens Max output: 128,000 tokens Knowledge cutoff: April 30, 2026 Input modalities: Text and images Output: Text only (no audio or video) Reasoning...

AI Adoption Is Accelerating, but Governance Is Lagging AI adoption is spreading quickly through government offices, schools, media companies, and ordinary business operations. The harder question is whether the rules and responsibilities around that adoption are keeping up. One report says the Pentagon is giving more than 3 million military and civilian personnel access to ChatGPT and Grok through a secure AI platform. If accurate, the scale is striking. It shows that AI is no longer being treated only as an experimental tool for small innovation teams. Large deployments also raise practical questions. Who approves sensitive uses? Which information can be entered into a system? How are inaccurate answers caught? And who is accountable when an AI-generated recommendation causes harm? Schools are taking different paths Education is becoming one of the clearest examples of this uncertainty. Some institutions are introducing AI literacy, teaching students how to check outputs and use assis...

The AI Infrastructure Race Gets More Expensive The AI boom is no longer just a competition between model makers. It’s also a race to secure the chips, servers, data centers, and money needed to run those models at scale. This week’s reports point to Nvidia considering an investment of more than $30 billion in Perplexity. The claim has not been treated here as a confirmed transaction, but even the possibility shows how closely model companies, search products, and chip suppliers are becoming linked. Nvidia’s position remains central because advanced AI systems need enormous computing capacity. As companies add assistants, agents, image tools, cybersecurity products, and enterprise automation, demand shifts from training experiments to permanent production infrastructure. Servers are becoming strategic Dell and other technology companies are reportedly seeing stronger orders for AI-ready servers. These machines are built around powerful accelerators, fast networking, and large memory poo...

AI Moves From Chatbots to Real-World Work The most interesting AI stories this week aren’t all about a new chatbot. They’re about software and machines being asked to do useful, physical work. Reported examples include drones transporting diagnostic samples, driverless Frito-Lay trucks, and AI-guided flight paths. In repair departments, copilots are helping technicians find procedures and diagnose problems. Rugged laptops equipped with GPUs are also being used in demanding environments, including Ukraine. These examples look unrelated at first. A medical drone, a delivery truck, an aircraft route, and a repair assistant don’t share the same hardware or workflow. What they do share is a shift in expectations: AI is increasingly being judged by the job it completes, not by how impressive its conversation sounds. From answering to acting Chat interfaces made AI visible to everyone, but many businesses need something more specific. They want a system that can inspect a document, identify a...

If you've ever worked with an AI coding agent like Antigravity across a real project, you've probably run into the same repeated problem: you keep re-typing the same instructions in every single prompt. "Don't hardcode values." "Cite the actual file and line." "Don't break existing tests." "Give me a table, not a wall of text." Eventually you realize these aren't one-off preferences — they're rules that should apply to every task, every time, without you having to restate them. Here's a practical set of global rules that came out of exactly that realization, refined across several real review and refactor tasks on a production codebase. Set these once as your agent's standing context, and your day-to-day prompts can shrink down to just the task itself. Why Global Rules Matter More Than Clever Prompts A single well-written prompt only helps once. The moment you start a new session, or hand off a task to a teammate u...