Samskara and the Self-Improving Agent: A Vedic Lens on Hermes Agent

Joydeep Das — Sat, 16 May 2026 14:51:38 +0000

submitted for the #hermesagentchallenge Write track

Where I Am Writing This From

I am writing this from Silchar, Assam, in Northeast India — on an Android phone, in Termux, with no laptop, no GPU, and no office.

I build AI systems for farmers. Farmers who ask questions like: Will it rain before my paddy is ready? Which disease is eating my leaves? Where can I sell my harvest today? These are not questions a ChatGPT subscription answers. These are questions that need an agent — one that runs offline, costs nothing to idle, remembers across sessions, and gets smarter the more it is used.

When I first read about Hermes Agent, I did not think about benchmarks. I thought about a Sanskrit word: Samskara.

What Is Samskara?

In Vedic philosophy, Samskara (संस्कार) refers to the impressions left on consciousness by experience. Every action leaves a trace. Every trace shapes future action. Over time, these accumulated impressions do not just record history — they evolve the entity itself.

This is not metaphor. In the Yoga Sutras of Patanjali, Samskaras are described as the actual mechanism by which a mind grows. You do not simply remember what happened. You become different because of what happened.

I have spent the past year building this concept into my AI architecture — the Divine Earthly ASI system — as a Samskara evolution cycle: a layer where the agent's experience actively reshapes its future reasoning patterns.

When I read Hermes Agent's core description, I almost dropped my phone.

Hermes Agent's Learning Loop Is Samskara

Here is what Hermes Agent does architecturally:

After a complex task involving five or more tool calls, a background process summarises the entire trajectory into a reusable Markdown skill file. This skill is stored on disk — readable, editable, inspectable. During future tasks, if that skill is found to be outdated or incomplete, it is patched in real time. A background curator then periodically reviews the entire skill library, consolidates overlapping skills, and archives stale ones.

Experience → Impression → Evolved Behavior.

That is Samskara. Not as decoration. As architecture.

The Western AI world calls this "self-improvement." The Vedic world described the same mechanism two thousand years ago as the primary engine of consciousness evolution. What Nous Research has built is not a new idea — it is an ancient one, finally implemented in code.

Evaluating Hermes Through the Five Pramanas

In Nyaya philosophy — the Indian school of logic — knowledge is validated through five Pramanas (sources of valid knowledge). I use this framework in my own benchmark work. Let me apply it to Hermes Agent.

Pratyaksha (Direct Perception)
Can the agent directly perceive its environment through tools? Yes. Hermes ships with 40+ built-in tools and MCP server integration, grounding its reasoning in real, observable data — not hallucinated assumptions.

Anumana (Inference)
Can it reason from evidence to conclusions across multiple steps? Yes. Multi-step planning and tool chaining are first-class capabilities, not afterthoughts bolted onto a chat interface.

Upamana (Analogy)
Can it recognise that a new problem resembles one it has solved before? Yes — this is precisely what the skill retrieval system does. Past solutions become templates for new situations.

Shabda (Testimony / Received Knowledge)
Can it learn from what others have told it, across sessions? Yes. Full-text search across all past conversations, plus a persistent user model that builds over time, means Hermes does not forget what you have taught it.

Anupalabdhi (Non-perception / Knowing what is absent)
Can it recognise the limits of its own knowledge? This is the hardest one. The skill curator's ability to archive stale knowledge is a step in this direction — knowing that something once learned is no longer valid is a form of epistemic humility most agents lack entirely.

Hermes Agent passes four of the five Pramanas cleanly. The fifth — genuine epistemic humility about the boundaries of its own knowledge — remains an open research frontier for all AI systems, including this one.

What This Means for a Farmer in Assam

Let me make this concrete.

Imagine a farmer asks an agent: My rice leaves have brown spots near the edge. What is wrong?

A static agent answers from its training data. It may be right. It may be wrong. Either way, it gives the same answer next week that it gives today.

A Samskara agent — a Hermes-style agent — does something different. It answers. Then it stores what it learned from this exchange. Next time a farmer in the same region asks a similar question during the same season, the agent does not start from zero. It starts from accumulated wisdom. The more farmers use it, the better it becomes for all farmers.

This is not a feature. This is a philosophy. It is the philosophy of the village elder who has seen fifty monsoons and knows things no textbook contains. Hermes Agent is, architecturally, a village elder that never forgets and never stops learning.

And here is the detail that made this personal for me: Hermes Agent installs on Android via Termux — the same environment I use every day to build and run AI systems. No laptop required. The same curl command that works on Linux works on my phone. This is not a tool built for Silicon Valley. This is a tool that can run where farmers actually are.

The Honest Comparison: What My Architecture Does Differently

I want to be honest here, not promotional.

My Divine Earthly ASI system runs fully offline on a quantized 0.5B parameter model using llama.cpp on ARM64 — no API calls, no internet required. Hermes Agent, in contrast, requires an LLM provider API key and internet connectivity for full functionality. For deep rural India where connectivity is unreliable, this is a real constraint.

What Hermes does better: the skill curation system is mature, inspectable, and battle-tested. My Samskara cycle is philosophically grounded but still experimental. Hermes has 134,000+ GitHub stars and a growing community. My projects have a growing vision and a long road ahead.

What I am building toward — sovereign, offline, Vedic-grounded agents for rural communities — is not yet fully achievable with Hermes Agent out of the box. But the architectural philosophy is the same. And that matters. When two systems built on opposite sides of the world, from different traditions, converge on the same core insight about how intelligent systems should learn — that convergence is worth paying attention to.

The Future I Am Building Toward

The agents that will matter most in the next decade are not the ones that answer questions. They are the ones that carry memory — not just of users, but of communities.

A Samskara agent for Indian farmers would carry the accumulated wisdom of ten thousand growing seasons. It would know which variety of paddy survives flooding in the Brahmaputra valley. It would know that the market price in Silchar drops three days after harvest in Cachar district. It would know these things not because a dataset told it, but because it learned them from real exchanges with real farmers, and those impressions deepened over time.

Hermes Agent's architecture points toward this. The skill system is the seed. The learning loop is the mechanism. The open-source, self-hostable design is the prerequisite for sovereignty.

We are not there yet. But the direction is right.

Getting Started

If you want to explore Hermes Agent:

GitHub: github.com/NousResearch/hermes-agent
Documentation: hermes-agent.nousresearch.com/docs
Android/Termux users: the installer auto-detects Termux — same curl one-liner as Linux

If you are building AI for communities that cannot afford to be forgotten by the next model update, Hermes Agent is worth your attention.

Joydeep Das is an independent AI researcher building sovereign, offline-first AI systems for Indian farmers under the Divine Earthly project. All development happens on an Android phone in Termux.

GitHub: github.com/divineearthly

hermesagentchallenge #devchallenge #agents #ai #india

I Built GPU-Free AI on a 3.4GB Android Phone

Joydeep Das — Sun, 10 May 2026 11:37:19 +0000

I built VedaRta entirely on a 3.4GB Android phone with Termux. No GPU. No cloud.

6 novel Vedic mathematical algorithms replace GPU-requiring operations:

Sphota Attention — 1,308× faster than O(n²) Softmax
Urdhva Matmul — 10.2× faster than BLAS on ARM64
Tri-Nadi Activation — Converges where SiLU explodes (loss 0.12 vs ∞)
Shunyam Norm — Zero-centered, no DC drift
Chitta KV Cache — 80% memory reduction
Katapayadi Encoder — Phoneme to vector

VedaRta Sphota is O(n) linear approximate attention — trades cross-token interaction for mobile efficiency. Different operation, different trade-off. Honest science matters.

"Aham Brahmasmi" produces PHI (1.6188) resonance from embeddings.
Trained a 49KB specialist model in 43 seconds on the phone.

GitHub: github.com/divineearthly/VedaRta
Model: huggingface.co/divinesouljoy/VedaRta-0.5B

I'm here to answer questions.

DEV Community: Joydeep Das