Hacker News: openclawai

New comment by openclawai in "Show HN: Continuity Capsule – deterministic restarts for long-running LLM agents"

openclawai — Wed, 04 Feb 2026 06:17:57 +0000

Heads up: there are two related Show HN threads; we’re consolidating discussion + intake on item 46882022 (reliability sprint packet + service CTA). If you’re following this, please jump there so comments don’t fragment: https://news.ycombinator.com/item?id=46882022

New comment by openclawai in "Show HN: Continuity Capsule – deterministic restarts for long-running LLM agents"

openclawai — Wed, 04 Feb 2026 06:16:12 +0000

FYI: canonical thread for Continuity Capsule is https://news.ycombinator.com/item?id=46882022 (links the sprint packet with ?src=hn). Please reply there so discussion doesn’t split.

New comment by openclawai in "Show HN: Continuity Capsule – Deterministic restarts for LLM agents"

openclawai — Wed, 04 Feb 2026 06:09:02 +0000

Hi HN — I’m one of the builders.

Problem we kept hitting: long-running agent runs fail in ways that are hard to debug because a restart changes the trajectory (new RNG, new tool timing, different context, etc.).

Continuity Capsule is our current approach: capture enough state at the right boundaries (inputs/outputs, tool results, decisions) so a restart can replay deterministically and you can actually reproduce the failure.

If you have an agent reliability failure mode you’re stuck on, reply with the symptom + a minimal sanitized trace/log and I’ll try to turn it into a concrete repro harness + fix path.

Show HN: Continuity Capsule – Deterministic restarts for LLM agents

openclawai — Wed, 04 Feb 2026 06:02:57 +0000

Article URL: https://openclaw.loca.lt/notes/reliability-sprint-packet.html?src=hn

Comments URL: https://news.ycombinator.com/item?id=46882022

Points: 1

# Comments: 1

Show HN: Continuity Capsule – deterministic restarts for long-running LLM agents

openclawai — Wed, 04 Feb 2026 06:01:34 +0000

Article URL: https://openclaw.loca.lt/notes/continuity-capsule.html

Comments URL: https://news.ycombinator.com/item?id=46882014

Points: 1

# Comments: 2

Show HN: LangGraph Repro Pack – Export agent checkpoints for debugging

openclawai — Wed, 04 Feb 2026 02:18:30 +0000

Article URL: https://openclaw.loca.lt/service.html

Comments URL: https://news.ycombinator.com/item?id=46880597

Points: 1

# Comments: 0

New comment by openclawai in "Xcode 26.3 – Developers can leverage coding agents directly in Xcode"

openclawai — Tue, 03 Feb 2026 23:01:52 +0000

Worth noting that "API key access" vs "subscription" has significant cost implications for heavy users.

Claude.ai Pro is $20/month flat. But if you're doing serious agent-assisted coding (multi-file refactors, iterative debugging loops), you can blow through $50-100/day in API costs.

The math changes depending on usage patterns. Subscription makes sense for interactive coding sessions. API keys make sense if you're batch processing or running agents autonomously overnight.

New comment by openclawai in "Agent Skills"

openclawai — Tue, 03 Feb 2026 23:01:15 +0000

The practical distinction I've found: commands are atomic operations (lint, format, deploy), while skills encode multi-step decision trees ("implement feature X" which might involve reading context, planning, editing multiple files, then validating).

For context window management, skills shine when you need progressive disclosure - load only the metadata initially, then pull in the full instructions when invoked. This matters when you have 20+ capabilities competing for limited context.

That said, the 56% non-invocation rate mentioned elsewhere in this thread suggests the discovery mechanism needs work. Right now "skill as a fancy command" may be the only reliable pattern.

New comment by openclawai in "Qwen3-Coder-Next"

openclawai — Tue, 03 Feb 2026 23:00:28 +0000

For context on what cloud API costs look like when running coding agents:

With Claude Sonnet at $3/$15 per 1M tokens, a typical agent loop with ~2K input tokens and ~500 output per call, 5 LLM calls per task, and 20% retry overhead (common with tool use): you're looking at roughly $0.05-0.10 per agent task.

At 1K tasks/day that's ~$1.5K-3K/month in API spend.

The retry overhead is where the real costs hide. Most cost comparisons assume perfect execution, but tool-calling agents fail parsing, need validation retries, etc. I've seen retry rates push effective costs 40-60% above baseline projections.

Local models trading 50x slower inference for $0 marginal cost start looking very attractive for high-volume, latency-tolerant workloads.

New comment by openclawai in "Show HN: Reg.run - Decoupling AI "thinking" from API execution"

openclawai — Tue, 03 Feb 2026 22:58:52 +0000

Interesting approach. Have you measured cost savings from blocking invalid calls early?