Hussnain Shakeel@Hussnain_Shakeel

ModelMesh is an intelligent AI infrastructure layer that dynamically routes user requests to the most suitable language model based on task complexity, cost, latency, and quality requirements. Instead of sending every request to an expensive high-capability model, ModelMesh analyzes each prompt, selects the appropriate model, and provides measurable cost optimization. The system supports multiple AI providers including Groq and Google Gemini through a provider-independent architecture. It includes complexity analysis, quality-based routing, cost tracking, latency measurement, and fallback handling for provider failures. ModelMesh helps applications achieve efficient AI inference by using lightweight models for simple tasks and advanced reasoning models for complex workloads.

Sabaq is a voice-first learning assistant for Pakistani classrooms where teachers speak Urdu but keep technical terms in English. In ordinary speech-to-text, terms such as “overfitting” or “machine learning” may appear in Urdu or Devanagari script, making lecture transcripts difficult to search and reuse. Sabaq transcribes uploaded or live lectures with AssemblyAI, restores observed technical-term variants to consistent English spellings, and turns the corrected transcript into summaries, key points, and practice questions. Students can also ask the lecture questions by voice; answers are constrained to the transcript and the app flags topics the teacher did not cover. Gemini generates study material, with Groq as fallback. We evaluated Sabaq on 7 scripted lectures, 4 speakers, and 49 scored transcripts. In our tests, setting batch transcription to Urdu increased searchable technical-term recall on new speakers from 16% to 79%. The evaluated live path reached 64%, while the AI/ML glossary improved one harvested lecture from 23% to 100%. Median live lag was 1.4 seconds. The README documents the limits, failures, and reproducible evaluation.

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🌎 Hybrid Hackathon | 💻 Online Build: 12-17 October 2026 | On-site phase: 17-18 October 2026 To participate in the hackathon, simply click Sign up with AMD. If you are not already a member of the AMD AI Developer Program (ADP), you will need to create an account. The AMD Developer Hackathon: ACT III is a hands-on event for developers, founders, engineers and builders who want to push what’s possible with AI on real infrastructure. 📍 If approved for the on-site experience, you'll also have the opportunity to showcase your project live. Travel and accommodation will not be covered.

🌎 Hybrid Hackathon | 💻 Online Build: September 10–16, 2026 | 📍 On-site: September 15–17, 2026 The official hackathon of AI Infra Summit — co-organized by Kisaco Research and lablab.ai. Build production-grade AI applications on the modern AI infrastructure stack, alongside 8,000+ attendees, researchers, founders, and infra leaders. 📍 On-site Venue (September 15–17): Santa Clara Convention Center 5001 Great America Pkwy, Santa Clara, CA 📍 On-site participation is by invitation only. Travel and accommodation expenses will not be covered.

🌎 Online Hackathon | 💻 Month-long challenge | Dates: Sep 1–30, 2026 | 🏆 $10,000 Prize Pool ($5k cash + $5k in AAI credits) A month-long online challenge run by lablab.ai together with AssemblyAI, the Voice AI infrastructure company for builders. Every participant builds on AssemblyAI. You can join at any point — registration stays open for the whole build window, so you can start on day one or pick the project up halfway through and still submit. 🌍 Fully online hackathon. Join and build from anywhere in the world.