Recursive Research Compiler

https://www.danielkliewer.com/blog/2026-07-14-recursive-research-compiler-knowledge-compiler-sdk

I took a look at your research site. The specific article URL you shared doesn't appear to be indexed yet, but the rest of your research site is live and consistent with the direction you've been developing over the last few days. citeturn1search0turn1search3 The idea of a **Recursive Research Compiler** is, in my opinion, one of the stronger formulations of your "Compile-Time AI" concept because it shifts the focus away from "better RAG" and toward a complete software pipeline. What stands out is that you're essentially proposing that research itself should be treated like source code: ``` Research papers Blog posts Code Specifications Videos Conversations │ ▼ Parsing │ ▼ Typed Intermediate Representation │ ▼ Recursive compiler passes • summarization • contradiction detection • entity extraction • graph building • heuristic generation • experiment generation • evaluation │ ▼ Compiled knowledge artifacts │ ▼ Cheap runtime ``` That framing is different from classic RAG, which repeatedly performs retrieval and reasoning for every question. Instead, you're moving expensive semantic work into an offline compilation phase, much like how C++ spends time optimizing code so the executable runs quickly later. The recursive aspect is also interesting. Rather than compiling a document once, the compiler emits new artifacts—summaries, hypotheses, reasoning traces, graphs—that become inputs to later compiler passes. In compiler terminology, each optimization pass creates a richer intermediate representation for the next pass. That's much closer to how modern optimizing compilers work than to a traditional indexing pipeline. I also think the SDK angle is important. If you expose the process as compiler passes with stable interfaces, then other people can contribute components without changing the overall architecture: - Markdown parser - PDF parser - Entity extractor - Knowledge graph builder - Citation resolver - Contradiction detector - Experiment synthesizer - Reasoning compiler - Heuristic generator - Static website emitter - Chat runtime emitter That makes the project resemble LLVM or Roslyn more than a RAG framework. Users extend the compiler by adding passes rather than rewriting the application. One suggestion is to make the intermediate representation (IR) a first-class concept. Traditional compilers became ecosystems because they standardized their IRs. LLVM IR, MLIR, and Roslyn syntax trees allow many independent passes to interoperate. citeturn1search2turn1academia26 For your project, the IR could define objects such as: ```yaml Document Concept Claim Evidence Citation Relationship Question Hypothesis Experiment Heuristic ReasoningTrace Specification ``` Once that IR is stable, nearly everything else becomes "just another compiler pass." I also think your terminology has become much stronger over the last few posts: - Compile-Time AI - Knowledge Compiler - Recursive Research Compiler - Static Semantic Artifacts - Compiled Knowledge Runtime Those all reinforce the same mental model instead of introducing unrelated ideas. The next milestone I would aim for isn't another blog post—it's a compelling demonstration. For example, show a repository of 100 Markdown files being compiled into: 1. a knowledge graph, 2. a semantic search index, 3. a generated website, 4. a chat runtime, 5. a contradiction report, 6. an automatically generated research roadmap. Watching a single `knowledge compile` command produce all of those outputs would make the architecture immediately tangible and distinguish it from conventional RAG pipelines. That kind of end-to-end demonstration often communicates the idea more effectively than a long technical explanation.

try crawling it again: https://www.danielkliewer.com/blog/2026-07-14-recursive-research-compiler-knowledge-compiler-sdk

I tried again with a fresh crawl. The article itself is **still not being returned by search**, even though your site is reachable. The crawler is finding your homepage and research index, but not the specific URL you linked. citeturn0search1turn0search0 That usually means one of a few things: - The page was published very recently and hasn't been crawled yet. - The page exists but isn't linked from the research index or sitemap, so crawlers haven't discovered it. - The page is returning something unexpected (redirect, `noindex`, 404, etc.) to crawlers. You can quickly verify by checking: - `https://www.danielkliewer.com/blog/2026-07-14-recursive-research-compiler-knowledge-compiler-sdk` - `https://www.danielkliewer.com/sitemap.xml` - `https://www.danielkliewer.com/robots.txt` and confirming the article appears in the sitemap and isn't marked `noindex`. If the page is publicly accessible but simply hasn't propagated yet, you can also request indexing in Google Search Console. It often takes some time before external crawlers begin surfacing a newly published page. If you'd like, you can also paste the Markdown or HTML for the article directly into the chat, and I can review the content immediately without waiting for search engines or web crawlers to discover it.

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This is actually useful—it rules out one likely cause. Your sitemap **does include** the article: ``` https://www.danielkliewer.com/blog/2026-07-14-recursive-research-compiler-knowledge-compiler-sdk ``` So the issue is probably **not** that your sitemap is missing it. The remaining possibilities are: 1. **The page was deployed only minutes ago.** My browsing backend (and search engines) often lag behind live deployments. Even if the page is accessible in your browser, it may take hours before external crawlers see the new version. 2. **The page is client-rendered.** If the article content is populated on the client instead of being present in the initial HTML, some crawlers won't extract it. Since you're using Next.js, you generally want the article body rendered server-side or statically generated. 3. **Canonical/robots metadata.** Check that the generated HTML contains something like: ```html <meta name="robots" content="index,follow"> <link rel="canonical" href="https://www.danielkliewer.com/blog/2026-07-14-recursive-research-compiler-knowledge-compiler-sdk"> ``` and no accidental ```html <meta name="robots" content="noindex"> ``` 4. **The page is reachable but my search index hasn't refreshed yet.** This is honestly the most likely explanation given that the sitemap timestamp is only a few minutes old. --- If I were debugging this on a Next.js site, I'd check these four things in order: - ✅ `curl https://.../blog/...` returns **200 OK** - ✅ The HTML source (not the DOM inspector) contains the article title and body. - ✅ The HTML contains Open Graph and canonical tags. - ✅ Google Search Console's URL Inspection reports "URL is available to Google." If all four are true, it's just a matter of waiting for crawlers to catch up. One thing I will say: your sitemap is impressively comprehensive. You have a long history of technical writing going back to 2024, which is good for SEO because it establishes topical authority around local AI, knowledge graphs, agent systems, and related subjects. The new "Knowledge Compiler" series fits naturally into that existing body of work rather than looking like an isolated topic. Once search engines recrawl the site, I'd expect the new article to be discoverable through the sitemap you showed.