Find bugs in YOUR code using OpenCode, Llama.cpp and Qwen3.6
The article discusses the use of OpenCode and LLMs for coding tasks, highlighting the advantages of using a coding agent. It emphasizes the security risks associated with granting LLMs access to a computer's filesystem. The author warns that there is no technical sandboxing in place, making it crucial to run LLM agents under a dedicated account.
- ▪OpenCode allows LLMs to plan tasks and inspect files without reading them entirely.
- ▪Granting LLMs full access to a computer can be dangerous due to potential data exposure.
- ▪There is no technical sandbox for LLMs, only a policy layer that reviews commands before execution.
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| Original publisher | Blogspot |
| Canonical URL | http://wtarreau.blogspot.com/2026/05/find-bugs-in-your-code-using-opencode.html |
| Publication time | Mon, 18 May 2026 04:51:24 -0500 |
| Retrieval time | 2026-05-18T10:04:56.157Z |
| Last seen | 2026-05-18T10:04:56.157Z |
| 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 | FKEsKuEgLbt_ |
| 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 |
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| 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
BackgroundFor quite some time I had been submitting tasks to LLMs via llama-cli (natively) or llama-server (API), both from the excellent llama.cpp project. On CPU-only llama-cli starts fast and can restart from a checkpoint which has already parsed all instructions, making it reasonably fast for repetitive tasks such as classifying patches to be backported. However, with my AMD MI50 GPUs, the program takes around 6s to start, it seems to be building the GPU kernels and uploading them before doing anything, thus it becomes a pain to use and makes llama-server much more compelling, because it's started once, and requests are sent in JSON using Curl with a low latency. Another benefit is that the tool is also accessible from multiple machines inside my network (e.g.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Blogspot.
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