Show HN: Adaptive Runtime – AI agent layer, no GPU, crash recovery
Adaptive Runtime introduces a new intelligence layer designed for stateful AI systems, addressing common runtime issues faced by AI agents in production. It provides solutions for crash recovery, memory retention, and decision-making confidence, ensuring that AI agents can adapt to changing conditions. The system operates without the need for GPUs and can run on minimal infrastructure, making it accessible for various applications.
- ▪Adaptive Runtime is not a chatbot framework or LLM wrapper, but an intelligence layer for AI systems.
- ▪It addresses runtime problems such as crash recovery, memory loss, and chaotic retries.
- ▪The system automatically analyzes conditions, calculates confidence, and selects actions to maintain stability.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,270 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/stateflow-dev/adaptive-runtime |
| Publication time | Fri, 29 May 2026 11:30:31 +0000 |
| Retrieval time | 2026-05-29T11:40:00.335Z |
| Last seen | 2026-05-29T11:40:00.335Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | EDoS4XGGlj-B |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Adaptive Runtime Runtime Intelligence Layer for Stateful AI Systems Not a chatbot framework. Not an LLM wrapper. Not a workflow builder. An adaptive runtime intelligence layer — the missing piece between your AI logic and production reality. The Problem Most AI frameworks solve the model problem. Nobody solves the runtime problem. Your AI agent in development: Works perfectly. Your AI agent in production: Crashes. Forgets state. Retries blindly. Dies silently. Production AI systems fail because of: 💥 No crash recovery — state lost on restart 🧠 No memory — agent forgets context between sessions 🔁 Retry chaos — blind retries with no back-off 📉 No confidence scoring — decisions made without certainty 🌊 No contextual awareness — can't adapt to changing conditions Adaptive Runtime fixes…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.