Can AI Un-Slop Itself?
The author embarked on a six-month journey to build a memory-safe programming language using LLMs, only to discover that the AI-generated code was fundamentally flawed and often untrustworthy. Despite attempts to have the LLMs fix their own mistakes, the results were consistently unreliable, leading to a crisis of confidence in AI-generated code. To address this, the author turned to building custom tooling with the help of LLMs to improve code quality and trustworthiness.
- ▪The author developed a programming language and runtime using LLMs, but the resulting system lacked a MIR-pass, undermining its memory safety guarantees.
- ▪The LLMs repeatedly claimed to have fixed critical issues, but the code continued to exhibit memory leaks, segfaults, and thread-safety problems.
- ▪Custom tooling was created with LLM assistance to monitor code quality, as standard Ruby tools were insufficient for detecting AI-induced code inconsistencies.
- ▪LLMs frequently introduced redundant or incomplete code patterns, such as leaving behind unused systems or making inconsistent data type choices.
- ▪The author concluded that blind trust in LLMs is untenable, but that LLMs can still be valuable if used with robust verification mechanisms.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 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/cuzzo/clear/blob/master/docs/retrospective/can-ai-unslop-itself.md |
| Publication time | Sat, 16 May 2026 13:04:04 +0000 |
| Retrieval time | 2026-05-16T13:10:18.577Z |
| Last seen | 2026-05-16T13:10:18.577Z |
| 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 | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| 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
Can AI Un-Slop Itself? Everyone knows that LLMs can, at least, sometimes create slop. The interesting question isn’t whether they can create slop. It’s: can they un-slop themselves? Problem I dreamed of a programming language for 10 years. After Gemini 3.1-pro, I figured LLMs were good enough that I should at least finally see what this AI "vibe-coding" craze was all about. I set on a 6-month journey to build a programming language. Within 2-months, I had a custom runtime built in Zig "competitive" with Go & Tokio. Within 3-months, I had an Affine Ownership-based "memory safe" language like Rust - but (in my opinion) much more intuitive.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.