Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.
| Date | Stars |
|---|---|
| 2026-07-31 | 4305 |
| 2026-08-02 | 4305 |
| 2026-08-05 | 4312 |
| 2026-08-06 | 4312 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern: From Start to Scale This repo is part of the [Panaversity Certified Agentic & Robotic AI Engineer](https://panaversity.org/) program. You can also review the certification and course details in the [program guide](https://docs.google.com/document/d/1BygAckkfc_NFQnTfEM6qqUvPdlIHpNItmRtvfRMGp38/edit?usp=sharing). This repo provides learning material for Agentic AI and Cloud courses. Here’s a polished, professional rewrite you can use as a one-pager or slide—tight on wording, clear on stakes, and just a touch playful so it doesn’t read like it was written by a committee (no offense to committees 😄). # Our Agentic Strategy for Pakistan: Four Working Hypotheses Pakistan must place smart, early bets on the technologies and talent that will define the agentic AI era—because we intend to train **millions** of agentic-AI developers across the country and abroad, and launch startups at scale (ambitious, yes—but coffee is cheaper than regret). ## Hypothesis 1 — Agentic AI is the trajectory We believe the future of AI is **agentic**: systems that plan, coordinate tools, and take actions to deliver outcomes, not just answers (aka “from chat to getting things done”—and ideally without breaking anything valuable). This hypothesis guides our curriculum design, tooling choices, and venture focus. ## Hypothesis 2 — Cloud-native rails: Kubernetes × Dapr × Ray Our bet for large-scale agentic systems is a cloud-native stack: **Kubernetes** for orchestration, **Dapr** (Actors, Workflows, and Agents) for reliable micro-primitives, and **Ray** for elastic distributed compute. Together, these provide the building blocks for durable, observable, horizontally scalable agent swarms. ## Hypothesis 3 — The real blocker is the **learning gap** Most AI pilots fail not because the models are incapable, but because teams don’t know **how** to integrate AI into workflows, controls, and economics. Recent coverage of an MIT study reports that **\~95%** of enterprise gen-AI implementations show no measurable P\&L impact—largely due to poor problem selection and integration practices, not model quality. Our program is designed to close this gap with workflow design, safety guardrails, and ROI-first delivery. [An MIT report that 95% of AI pilots fail spooked investors. But it’s the reason why those pilots failed that should make the C-suite anxious](https://fortune.com/2025/08/21/an-mit-report-that-95-of-ai-pilots-fail-spooked-investors-but-the-reason-why-those-pilots-failed-is-what-should-make-the-c-suite-anxious/) ## Hypothesis 4 — The web is becoming **agentic and interoperable** The next web is a fabric of interoperable agents coordinating via open protocols—**MCP** for standardized tool/context access, **A2A** for authenticated agent-to-agent collaboration, and **NANDA** for identity, authorization, and verifiable audit. These emerging standards enable composable automation across apps, devices, and clouds—shifting the browser from a tab list to an **outcome orchestrator** with trust and consent built in (finally, fewer tabs, more results). --- ### What this means for execution * **Talent engine:** hands-on training in agentic patterns (planning, tools, memory, evaluation), workflow design, and safety—tied to real industry use-cases (because “Hello, World” doesn’t move P\&L). * **Reference stack:** Kubernetes + Dapr + Ray blueprints with observability, guardrails, and cost controls—shippable by small teams (and auditable by large ones). * **Protocol readiness:** MCP/A2A/NANDA-aware agent designs to ensure our solutions interoperate as the standards mature (future-proof beats future-guess). If any hypothesis is wrong, we’ll measure, publish, and pivot fast—because the only unforgivable error is not learning. ## This Panaversity Initiative Tackles the Critical Challenge: **“How do we design AI Agents that can handle 10 million concurrent AI Agents without failing?”**
Excerpt of 21,180 characters
Read on GitHub
1.4k
Muhammad Junaid
301
Sir Qasim · CancerClarity LLC, NYC, USA · United States
107
86
Ameen Alam · panacloud · Pakistan
73
M Rehan Ul Haq · Panaversity · Pakistan
19
19
15
7
5
4
4
4
2
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dd19e32e6cde72e3, topic:agentic-ai, name:agentic, desc:agentic
matched fp:dd19e32e6cde72e3, topic:kubernetes
matched fp:dd19e32e6cde72e3, topic:mcp