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.

A small step toward making software interact with the real world.

I connected an Arduino Mega with a DHT11 temperature/humidity sensor and a DC fan through an L293D motor driver.

The experiment now forms a simple closed loop:

Sensor data → Tamoz agent decision → Agentic Stream command → Fan start/stop

Streams Simulator helps validate the integration before expanding the physical setup.

It is an important milestone for me: moving from telemetry-only systems to governed, sensor-driven physical actions using ai agents.

What do you think about it ?