Show HN: Thaw – Git branch for a running LLM (fork agents, skip prefill)
Thaw is a new tool designed for AI agents that allows them to fork multiple branches for problem exploration while sharing memory. This innovation enables faster processing by skipping the cold prefill stage and running divergent branches concurrently. It is particularly beneficial for reinforcement learning teams and coding-agent teams, significantly reducing the time and resources required for training and exploration.
- ▪Thaw allows AI agents to fork multiple branches from a shared memory, improving efficiency in problem exploration.
- ▪The tool can reduce the time taken for reinforcement learning rollouts from around 340 seconds to just 0.88 seconds.
- ▪Thaw is open source and compatible with existing frameworks like vLLM and SGLang.
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/thaw-ai/thaw |
| Publication time | Sat, 30 May 2026 22:07:26 +0000 |
| Retrieval time | 2026-05-30T22:22:44.413Z |
| Last seen | 2026-05-30T22:22:44.413Z |
| 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 | auqqDpWqxFbc |
| 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
thaw The fork primitive for AI agents. When your agent forks N ways to explore a problem, thaw skips the cold prefill and runs them in parallel from one shared memory. Snapshot a running session — weights, KV cache, scheduler state, prefix-hash table — and hydrate N divergent children at the fork point. git branch for live AI agents. pip install thaw-vllm The receipt — ForkPool, 2026-04-20 Pre-warmed subprocess pool holds the engine once; each fork_completions() call snapshots KV only. Llama-3.1-8B on H100 80 GB PCIe, 5 rounds × 4 branches × 64 tokens: Stage Time init_pool (one-time — workers boot with real weights) 22.3s First fork round 1.16s Median fork round 0.88s Per-round cost: ~340s cold-boot → sub-second (≈400× amortized). All rounds 4/4 non-empty and divergent.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.