Curated Open-Source AI Repositories

“Unified interface for OpenAI, Anthropic, Gemini, Groq, and Bedrock with automatic fallback switching when an API is down.”

“I tested OmniRoute as a LiteLLM alternative from Junagadh — routing DeepSeek to Gemini with 3-line config held 85% savings. When NOT to use: for Pydantic-heavy tool validation keep LiteLLM.”

“My tool grounding bible — I map every new MCP tool to this list before writing a Pydantic schema. When NOT: for curated MCP servers use Glama ranking instead.”

“My onboarding curriculum for Surat textile founders — 12 Jupyter notebooks that map to our MCP + Pydantic pipeline. When NOT: for prod harness use MAF instead.”

“Instead of copying agent prompts, you embed Copilot’s own agentic workflow SDK — April public preview kept it trending Aug 1 at #6 because it turns any app into a Copilot-native agent host. Rating: 4.6/5. When NOT to use: Avoid if you require 100% open-weight inference — the SDK is Copilot-coupled and assumes GitHub-hosted model routing.”

“My Junagadh edge boxes run Pi5/4070 mixes — llmfit replaces guesswork with real fit + tok/s estimates across Q2_K→Q8_0. v1.1.12 now PRs measured tok/s back, which is why it held 34.5k stars and Repo Radar #2 Aug 28. Rating: 4.8/5. When NOT to use: Not for pure cloud API routing — llmfit shines for local/self-hosted sizing, not for OpenAI-only bill tracking.”

“My hybrid router keeps 80% on SLM Pi5; semantic-router is the production-grade version — neural domain classifiers, multi-backend. 5,376 stars Go, Apache-2.0, and the missing layer under Maka/llmfit is why it trended Aug 28. Rating: 4.5/5. When NOT to use: Skip if you route by static rules — gains appear when prompt intent actually needs classifier, not simple keyword.”

“I build offline-first agents for Junagadh where 4G drops — minimind lets me pre-train a 115M model on a ₹6K VPS and run 62 tok/s on Pi 5. Its from-scratch recipe demystifies tokenization and MoE better than any course.”

“I run P95 42ms HNSW + 90-day OTel ledger from Junagadh where every token counts — context-mode cut my Claude Code context by 98% sandboxed and stopped re-reads after /compact. Its MCP hook routing across 17 platforms is the missing persistence layer I built manually for Gujarat SMEs on Pi 5 62 tok/s.”

“I run WhatsApp-first vernacular voice agents (Hindi/Gujarati) for Gujarat SMEs — VoiceStudio gave me a local-first TTS stack that runs 62 tok/s on Pi 5 without cloud latency. Its voice-cloning pipeline mirrors my HITL ledger: generate → OPA gate → human approve before irreversible Razorpay.”

“I publish 30 journal posts/month via Curro where Google AI Overviews cites extractable Indian sources — humanizer taught me how detector evasion fails EEAT. I use its style-transfer eval to harden my own audit-blog.mjs anti-fluff check before publishing from Junagadh.”

“I forecast Gujarat SME demand (textile seasonality, UPI retry windows) — TimesFM lets me do zero-shot forecasting on Pi 5 without training, fitting my offline-first 78% on-device rule. Its foundation model beats ARIMA when my Junagadh data has gaps.”

“I run 70B offline on laptop and Pi 5 at 62 tok/s where every dependency is a failure point — colibri streams MoE experts from disk in pure C with zero deps, the same no-dependency discipline I enforce on ₹6K VPS deploys. Its run-on-hardware-you-own shape is exactly how I keep 78% of Gujarat SME calls inside the VPC.”

“I write system prompts for governed agents where one leaked instruction pattern teaches more than ten tutorials — this repo shows exactly how frontier labs structure tool-use, HITL, and refusal rails. I study these prompts from Junagadh to harden my own Pydantic + OPA harnesses against the same injection classes.”

“I track token burn per agent run in my P95 ledger where verbose tool outputs eat margin on fixed-price Gujarat SME builds. Caveman cut my Claude Code review loops by roughly half in Junagadh trials, and I keep it as a proxy gate in front of expensive reasoning calls on the VPS.”

“I benchmark 70B offline on laptop where long-horizon repo sessions die from context loss. Kimi K3 holding 1M context with native vision for CAD plus kernel work is the open-weight I point Surat clients to for overnight agent runs, and I pair it with my trace-MCP harness to keep tool calls inside budget.”