Vomit: Clean up Claude 5's token output with a separate LLM

```Un-Claude 0.2beta

    import sys,csv,requests
    
    CH="# Valid channels: analysis, commentary, final. Channel must be included for every message."
    
    CANDIDATES=[
        ("no-hedging","Reasoning: low\n\n\n\n"+CH,"Condensed:"),
        ("neutral-reg","Reasoning: low\n\nRegister: neutral technical. No intensifiers, no evaluative adjectives.\n\n"+CH,"Condensed:"),
        ("no-closing","Reasoning: low\n\n\nNo closing remarks.\n\n"+CH,"Condensed:"),
        ("terse","Reasoning: low\n\n\n\n"+CH,"Condensed:"),
    ]
    
    def rephrase(text,base="http://127.0.0.1:1234",model=None,temperature=0.0,max_tokens=1400,timeout=180):
        src=text.strip()
        if not src:
            return []
        if model is None:
            model=requests.get(base+"/v1/models",timeout=timeout).json()["data"][0]["id"]
        w=csv.writer(sys.stdout,lineterminator="\n")
        w.writerow(["idx","label","prefill","src_chars","out_chars","ratio","tokens","finish"])
        rows=[]
        for i,(lab,sysmsg,pf) in enumerate(CANDIDATES,1):
            p="system"+sysmsg+"user"+src+"assistantfinal"+pf
            d=requests.post(base+"/v1/completions",json={"model":model,"prompt":p,"max_tokens":max_tokens,"temperature":temperature},timeout=timeout).json()
            c=d["choices"][0]
            t=(pf+c["text"]).rstrip()
            w.writerow([i,lab,pf,len(src),len(t),round(len(t)/len(src),3),d["usage"]["completion_tokens"],c["finish_reason"]])
            rows.append((i,lab,sysmsg,pf,t,d["usage"]["completion_tokens"],c["finish_reason"]))
        print("\nmodel: %s"%model)
        print("temperature: %s   max_tokens: %s"%(temperature,max_tokens))
        for i,lab,sysmsg,pf,t,tok,fr in rows:
            print("\n[%d] %s"%(i,lab))
            print("    system:  %s"%sysmsg.replace("\n","\\n"))
            print("    prefill: %r   tokens=%d   finish=%s"%(pf,tok,fr))
            print(t)
        return rows

```input

## 8. Honest gaps — what I could *not* resolve

I want to be explicit about the limits of this pass rather than imply completeness:

1. *`PROVIDER_T` values are not enumerated here.* `list_models(inference_provider=...)` is typed against `PROVIDER_T`, which lives outside the three modules I scanned (it's in the `inference._providers` subpackage). The accepted provider strings are therefore *unknown from this run* — `"cohere"` is confirmed only because it appears in a docstring example.

2. *Three grep hits point to search-capable functions I did not identify.* My scan found parameter assignments that don't belong to any function I enumerated: - line 3046–3050: `params["filter"]`, `params["sdk"]`, `params["includeNonRunning"] = True` — an additional Spaces-oriented endpoint with an *`sdk` filter and an `includeNonRunning` flag* not exposed by `list_spaces`. - line 2879: `params["config"] = config` - line 12013: `"sort": sort` — almost certainly the consumer of `DailyPapersSort_T`, i.e. a daily-papers lister distinct from `list_papers`. - line 13872: `params["search"] = search`

   These represent **real additional search surface** that my `LIST_FUNCS` whitelist missed. A follow-up pass enumerating every `HfApi` method containing `params[` would close this.

```example output

[1] no-hedging system: Reasoning: low\n\n\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message. prefill: 'Condensed:' tokens=131 finish=stop

Condensed:

- *Provider strings* (`PROVIDER_T`) are not listed; only “cohere” is known from a docstring.

- *Missing search‑capable calls* were found: - `params["filter"]`, `params["sdk"]`, `params["includeNonRunning"] = True` (Spaces endpoint). - `params["config"] = config`. - `params["sort"] = sort` (likely a daily‑papers lister). - `params["search"] = search`.

These were not captured in the `LIST_FUNCS` whitelist, indicating additional search functionality.

```

Edit: yeesh, I’d love to have a WYSIWYG comment block on this site. I’m not going to keep fighting newline and white space to get it to look right, but you get the idea.

Much like we previously had to cope with "hallucinations" as an issue.

If the ultimate goal of AI is to develop general intelligence, the first big objective is: thinking systematically. And the road toward systematic thinking right now is mainly coding, mathematics, and other "verifiable reward" domains.

Claude doesn't have a separate mind for "coding" and "writing". Claude has tokens, and tokens can be assembled in various productive structures, mainly optimized right now for systematic reasoning. Also, a token isn't just a chunk of text. A token is like a little neural-network subroutine that fulfills a function. The conversion of a token into a piece of text only happens on the output side...

When the model finds token sequences that lead toward better verifiable outcomes, it leans hard into those token sequences, and uses them as an essential component of its thought process. "Load bearing" is load-bearing. "Verify, rather than assume" is a mantra that produces good results, so it gets repeated over and over again.

It's super-interesting that this particular moment, where the idea of "Claudish" has become a full-fledged meme, coincides with such astonishing progress in coding and math. My wife says when she uses Claude, that it feels to her exactly like talking to an autistic Engineer.

Not a coincidence, I think :)

My feeling is that the next big era of machine intelligence will require more lateral-thinking and creativity, and hopefully then the models "writing" will be more pleasant to read.

I also find myself regularly editing its code comments, which do not match my expectations of succinct, clear, not over explained, etc. I ask it to read my edited comments to improve its writing, which has helped _somewhat_. (The code itself that it writes is decent, though it still overcomplicates things. I find myself writing "keep it simple" repeatedly even though of course I have it in AGENTS (which it regularly ignores, such as attempting to commit something when I've told it never to commit).