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arkonrad

Re:Ask HN: What are you working on? (September 2025) on Tuesday September 30, 2025 @08:39AM (#45424699)
Early stage with ARK Cloud API & Stateful LLM sessions without input token costs. Conversation history is kept server-side, so you only send new messages. Demo and docs: https://ark-labs.cloud/documentation

Looking for feedback on use cases and session controls (machine2machine).

A great map showing how governments/armies interfere with navigation systems — and the regions they target are no coincidence.
On cookies: we use an HTTP cookie (ark_session_id) purely as an opaque session identifier. The cookie is how the client ties subsequent requests to the same pinned session/worker/GPUs on the provider side so the provider can keep the model activations/state in GPU memory between calls. Not a magic for the model; it’s a routing key that enables true session affinity.

On “thinking steps” and contamination: good point - naively persisting raw chain-of-thought tokens can degrade outputs. ARKLABS Stateful approach is not a blanket “store everything” policy.

And my criticism targets higher-level provider practices: things like response caching, aggressive prompt-matching / deduplication heuristics, or systems that return previously generated outputs when a new prompt is “similar enough.” Those high-level caches absolutely can produce the behaviour I described - a subtle prompt change that nevertheless gets routed to a cached reply.

The platform has been launched — we’re collecting data, but early results are very promising: we’re seeing linear complexity, lower latency, and ~80% input-token savings. At the same time we’d love to hear more feedback on whether this approach could be useful in real-world projects.

And about going against the grain, as you mentioned at the end… well — if startups didn’t think differently from everyone else, what would be the point of being a startup?

Show HN: Stateful LLM inference (no cost for input tokens, not prompt-caching)

Hi HN,I’ve been frustrated for a while with how LLM inference works in the cloud today. Every API call starts from scratch: you resend your entire prompt + conversation history, and you’re charged for every input token, even if the model has already “seen” that context before.This leads to two big problems:Performance & cost – constantly resending input tokens is wasteful.Quality loss –...

( Read More... | 2 comments | 2 points | on HN )
Re:Open models by OpenAI on Wednesday August 06, 2025 @09:57AM (#44812124)
Do you have your own platform to run inference?
Re:Open models by OpenAI on Wednesday August 06, 2025 @09:56AM (#44812105)
I’ve been leaning more toward open-source LLMs lately. They’re not as hyper-optimized for performance, which actually makes them feel more like the old-school OpenAI chats-you could just talk to them. Now it’s like you barely finish typing and the model already force-feeds you an answer. Feels like these newer models are over-tuned and kind of lost that conversational flow.
Re:Show HN: Stateful AI API with OS Models on Thursday July 31, 2025 @07:39AM (#44744590)
Whisper Large V3 Turbo

Show HN: Stateful AI API with OS Models

https://www.RogueFishMedia.com/

We just launched ARK Cloud API: OpenAI‑compatible, stateful AI inference service that keeps context across calls to slash token spend (0.01 USD / 1M input tokens), spins up in 10 s with Google SSO, runs fully on GDPR‑strong EU‑only infra, and gives 50 000 free credits at signup.Supports Stable Diffusion (cheapest rates), Llama 8 & 70B, embeddings, STT and more.PAYG, no logs, no data...

( Read More... | 3 comments | 4 points | on HN )