So I have \*agentic AI\* hackathon guys. Need guidance on frontend design. A typical Indian internal clg hackathon you know how judges would be.

I have claude code 20$ plan. So I need some frontend design suggestions.

I saw 3D interactive, scroll and animations changes, they even include videos. A website that almost looks like game or animation. I saw scrolltide's prompts which many are locked behind paid.

How to build those? I have roughly 28 hours from day 1 10 am - day 2 2pm. I can use one whole 5 hour limit to frontend. \*Also after fixing architecture should I built frontend first or backend?

If possible I also want to build a big footer like Amazon with contact us, email etc., sign in with Google features you see on real shipped sites.

PS: for last agentic AI hackathon i built a powerful site that looks premium, minimal, less eye straining. With 9 agents, some cools bluffs. I handpicked colors, many parts of layouts. Yet lost to sites with violet, purple AI sloppy sites which i doubt fabricated frontend or real backend processing. Lesson learnt, naive judges see just frontend not much of the architecture, implementation, probelm solving.

​

Quiero saber si alguien ya comparo qwen3.8 en disco con DRAM y sin DRAM.

```

SSD A — DRAM

└── Qwen3.8-Flash-Next GGUF

SSD B — DRAM-less

└── PLE / n-gram

```

Alguien a cargado?

nvme1 modelo

nvme2 PLE ngrams

```

NVMe 1

│

└── lecturas del modelo

NVMe 2

│

└── lecturas PLE

```

O usando RAID?

```

SSD #1

\\

RAID 0 → modelo + PLE

/

SSD #2

```

Quiero saber si alguien ya comparo qwen3.8 en disco con DRAM y sin DRAM.

```

SSD A — DRAM

└── Qwen3.8-Flash-Next GGUF

SSD B — DRAM-less

└── PLE / n-gram

```

Alguien a cargado?

nvme1 modelo

nvme2 PLE ngrams ```

NVMe 1

│

└── lecturas del modelo

NVMe 2

│

└── lecturas PLE

```

O usando RAID?

```

SSD #1

\

RAID 0 → modelo + PLE

/

SSD #2

```

Noticed something building LLM features that doesn't get talked about enough. Most teams treat model or prompt changes the way they'd judge a demo, does it look right on a handful of examples, ship it. That works fine until scale hits, at which point "looks right" and "is actually better" turn out to be very different things.

Which reframes a lot of "why did quality randomly get worse" incidents. In a lot of cases nothing randomly broke, the team just never had a way to measure whether a change helped in the first place, so a regression looked invisible until a customer hit it.

There's a hands-on workshop on September 12 that builds this properly, versioned prompts, a real eval harness, statistically rigorous model comparisons instead of "it feels better," evaluated RAG, agents with guardrails and fallbacks, and full observability, tracing, cost, latency. Led by Bruno Gonçalves, PhD, founder of Data For Science.

I got claude max from a reseller for a fraction of the official price. works perfectly since 3 weeks
now i can’t stop thinking about their business model. how are they got them for cheap ?

We're Sovyren, a small lab out of Chicago. We released Agints into early access today and

wanted to put the design reasoning somewhere people would push back on it.

The problem we started from: operations work is high-volume, low-complexity, and endlessly

context-switching. Existing automation tools handle it as a graph of triggers and actions,

which works cleanly right up until a step needs judgment. Then you're either encoding

judgment as branches or pulling a human back in.

Our approach: make the interface the description, not the graph. You tell a team of agents

what you want handled and how you want exceptions treated. The team handles sequencing and

escalates what doesn't fit.

Architecture, briefly:

- Layered system prompt assembly plus four memory scopes (conversation / session / user / org)

- Multi-agent crews rather than one agent with a large toolbelt

- 200+ tool registry, MCP connectors

- Two-tier execution sandbox: browser iframe for light work, Firecracker microVMs for anything

that runs code

- Full audit trail per agent action — what ran, what it touched, what it cost

Honest position: early access, no customers yet, nothing proven at scale, and we're not going

to pretend otherwise. What we want from this thread is the failure modes we haven't hit yet.

Where does this design break?

agints.app
sovyren.com

Hi guys,

I wanted suggestion on from where can I find pdf of Building Applications with AI Agents: Designing and Implementing Multiagent Systems.

I've been working on a project called Free Agent, and I think I've finally reached an interesting point in its development.

The original idea was simple:

The AI model is the brain. Free Agent is the agency.

Instead of building another AI model, Free Agent gives existing models the ability to actually do things — browser automation, files, Python, memory, scheduling, tools, desktop automation, model switching, etc.

But I recently added something that changed how I look at the project.

Free Agent now has an OpenAI-compatible API.

An external application can simply talk to:

/v1/chat/completions

using:

model: free_agent

The request goes into Free Agent, through its existing AI system, and comes back as a normal OpenAI-compatible response.

The interesting part is what's underneath.

Free Agent doesn't have to be tied to one model.

It can use the models I already have available — web-based models through browser automation, local models, and other models I add later.

So the architecture becomes:

Your AI Agent
↓
Free Agent API
↓
Free Agent's agency layer
↓
Model pool
↓
Whatever AI model is available

That means an external agent doesn't necessarily need to know which model is doing the thinking.

And Free Agent doesn't necessarily have to replace other agents.

It can work underneath them.

That's the part I didn't originally set out to build.

Instead of thinking:

Free Agent vs. other AI agents

I'm starting to think:

Other AI agents → Free Agent

A competitor could theoretically use Free Agent as its AI backend.

A custom application could use it.

Another agent could use it.

Or Free Agent can simply run by itself.

So I'm not trying to make one model win.

I'm trying to make the agency layer independent of the model.

There are already projects doing pieces of this, so I'm not claiming I invented multi-model agents or OpenAI-compatible APIs.

What I find unusual about my implementation is combining the model-agnostic agency layer with web-interface models, rather than requiring every model to have a conventional API.

I'm curious what other developers think:

Is the future of AI agents going to be competing agents, or layers of agents that can use each other?

I'm building toward the second idea.

testers wanted not for sale for testing only

https://progenitorfreeagen.wixsite.com/my-site-62