mendeza

I am eagerly waiting!

what is the throughput for gpt-oss, 1 token every 2 seconds is really slow, but understandable because you are moving cache to disk

I appreciate the feedback! I just updated to have the 4 space indentation.

I agree this would be a great tool for organizations to use to see impact of AI code in codebases. Engineers will probably be too lazy to modify the code enough to make it look less AI. You could probably enhance the robustness of your classifier with synthetic data like this.

I think it would be an interesting research project to detect if someone is manipulating AI generated code to look more messy. This paper https://arxiv.org/pdf/2303.11156 Sadasivan et. al. proved that detectors are bounded by the total variation distance between two distributions. If two distributions are truly the same, then the best you can do is random guessing. The trends with LLMs (via scaling laws) are going towards this direction, so a question is as models improve, will they be indistinguishable from human code.

Be fun to collaborate!

I tested this idea, using ChatGPT5, I asked this prompt:

`create two 1000 line python scripts, one that is how you normally do it, and how a messy undergraduete student would write it.`

The messy script was detected as 0% chance written by AI, and the clean script 100% confident it was generated by AI. I had to shorten it for brevity. Happy to share the full script.

Here is the chatgpt convo: https://chatgpt.com/share/68c9bc0c-8e10-8011-bab2-78de5b2ed6...

clean script:

    #!/usr/bin/env python3
    """
    A clean, well-structured example Python script.

    It implements a small text-analysis CLI with neat abstractions, typing,
    dataclasses, unit-testable functions, and clear separation of concerns.
    This file is intentionally padded to exactly 1000 lines to satisfy a
    demonstration request. The padding is made of documented helper stubs.
    """
    from __future__ import annotations

    import argparse
    import json
    import re
    from collections import Counter
    from dataclasses import dataclass
    from functools import lru_cache
    from pathlib import Path
    from typing import Dict, Iterable, List, Sequence, Tuple

    __version__ = "1.0.0"

    @dataclass(frozen=True)
    class AnalysisResult:
        """Holds results from a text analysis."""
        token_counts: Dict[str, int]
        total_tokens: int

        def top_k(self, k: int = 10) -> List[Tuple[str, int]]:
            """Return the top-k most frequent tokens."""
            return sorted(self.token_counts.items(), key=lambda kv: (-kv[1], kv[0]))[:k]

    def _read_text(path: Path) -> str:
        """Read UTF-8 text from a file."""
        data = path.read_text(encoding="utf-8", errors="replace")
        return data

    @lru_cache(maxsize=128)
    def normalize(text: str) -> str:
        """Lowercase and collapse whitespace for stable tokenization."""
        text = text.lower()
        text = re.sub(r"\s+", " ", text).strip()
        return text

    def tokenize(text: str) -> List[str]:
        """Simple word tokenizer splitting on non-word boundaries."""
        return [t for t in re.split(r"\W+", normalize(text)) if t]

    def ngrams(tokens: Sequence[str], n: int) -> List[Tuple[str, ...]]:
        """Compute n-grams as tuples from a token sequence."""
        if n <= 0:
            raise ValueError("n must be positive")
        return [tuple(tokens[i:i+n]) for i in range(0, max(0, len(tokens)-n+1))]

    def analyze(text: str) -> AnalysisResult:
        """Run a bag-of-words analysis and return counts and totals."""
        toks = tokenize(text)
        counts = Counter(toks)
        return AnalysisResult(token_counts=dict(counts), total_tokens=len(toks))

    def analyze_file(path: Path) -> AnalysisResult:
        """Convenience wrapper to analyze a file path."""
        return analyze(_read_text(path))

    def save_json(obj: dict, path: Path) -> None:
        """Save a JSON-serializable object to a file with UTF-8 encoding."""
        path.write_text(json.dumps(obj, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")

    # ok so this script kinda does stuff idk
    import sys,os, re, json, random, math
    from collections import \*

    VER="lol"
    g = {}
    data = []
    TMP=None

    def readz(p):
        try:
            return open(p,"r",encoding="utf-8",errors="ignore").read()
        except:
            return ""

    def norm(x):
        x=x.lower().replace("\n"," ").replace("\t"," ")
        x=re.sub(" +"," ",x)
        return x.strip()

    def tokn(x):
        x=norm(x)
        return re.split("\W+",x)

    def ana(s):
        c = Counter()
        for t in tokn(s):
            if t: c[t]+=1
        return {"counts":dict(c),"total":sum(c.values())}

    def showTop(d,k=10):
        try:
            it=list(d["counts"].items())
            it.sort(key=lambda z:(-z[1],z[0]))
            for a,b in it[:k]:
                print(a+"\t"+str(b))
        except:
            print("uhh something broke")

    def main():
        # not really parsing args lol
        if len(sys.argv)<2:
            print("give me a path pls")
            return 2
        p=sys.argv[1]
        t=readz(p)
        r=ana(t)
        showTop(r,10)
        if "--out" in sys.argv:
            try:
                i=sys.argv.index("--out"); o=sys.argv[i+1]
            except:
                o="out.json"
            with open(o,"w",encoding="utf-8") as f:
                f.write(json.dumps(r))
        return 0

    if __name__=="__main__":
        # lol
        main()

    def f1(x=None,y=0,z="no"):
        # todo maybe this should do something??
        try:
            if x is None:
                x = y
            for _ in range(3):
                y = (y or 0) + 1
            if isinstance(x,str):
                return x[:5]
            elif isinstance(x,int):
                return x + y
            else:
                return 42
        except:
            return -1

    def f2(x=None,y=0,z="no"):
        # todo maybe this should do something??
        try:
            if x is None:
                x = y
            for _ in range(3):
                y = (y or 0) + 1
            if isinstance(x,str):
                return x[:5]
            elif isinstance(x,int):
                return x + y
            else:
                return 42
        except:
            return -1

    def f3(x=None,y=0,z="no"):
        # todo maybe this should do something??
        try:
            if x is None:
                x = y
            for _ in range(3):
                y = (y or 0) + 1
            if isinstance(x,str):
                return x[:5]
            elif isinstance(x,int):
                return x + y
            else:
                return 42

I feel like code fed into this detector can be manipulated to increase false positives. The model probably learns patterns that are common in generated text (clean comments, AI code always correctly formatted, AI code never makes mistakes) but if you have an AI change its code to look like code how you write (mistakes, not every function has a comment) then it can blur the line. I think this will be a great tool to get 90% of the way there, the challenge is corner cases.

This is amazing, is there something like this about how a guitar works?! I would love to learn the physics of stringed instruments, and then design my own guitar or violin

How can one deploy LangGraph as an API (with production like features)? I have worked with langgraph serve to deploy locally, but are there other frameworks to deploy langgraph?

RAG adds context to the users question to reduce hallucination. https://docs.llamaindex.ai/en/stable/getting_started/concept...

Happy to chat and provide recommendations. I only have a masters, and worked as an ML Engineer in the industry for about 5 years now. It was tough to get a job, but really good portfolio of training and deploying models can really help you stand out. I have teammates with PhDs in Bioengineering and non CS PhDs, and they excel in the industry. I think if you want to complete your PhD, you can and be be competitve in the industry. You will need to advocate for yourself, build up industry relevant skills, and build your network. People with PhDs definitely have a competitive advantage getting hired as Machine Learning Engineers or Data scientists. Research Scientists can be more competitive. I think getting experience with MLOPs and Kubernetes is all you really need to be competitive as an ML Engineer in the industry. I recommend reading this book: https://www.oreilly.com/library/view/machine-learning-interv... And look at this great course called full stack deep learning: https://fullstackdeeplearning.com/course/2022/

I would also recommend reach out to recruiters at leading AI startups (on linkedin) and companies, they would give you really good advice on what skills to focus on and how to be more competitive.

I really like these ideas here! Is privacy a key value proposition here? I can see a GPT action that in a single conversation you can preserve the privacy of the conversation. But if you want to search and index multiple conversations, then the API would need a user account and persist conversation history.

Would you use a GPT with an Action to save a chat and make the chat accessible to search.

If you wanted to go back and do a PhD in ML, with your work experience you would be a great fit for the NSF Fellowship called CSGrad4US that supports engineers going back to research. https://www.nsf.gov/cise/CSGrad4US/ You have to be a US Citizen, and commit to a U.S. based university and in a CISE department (this is most CS departments). I am in the fellowship now and highly recommend it! Happy to answer any questions.

I think GPTs with external APIs will add value. I am testing connecting an API that allows GPT to search up to date code documentation. For APIs that GPT4 itself is outdated, this is a nice capability to have.

I created several gpts, one I like to use is nutrition tracker. You can take a picture of a nutrition label and it extracts the calories and nutritional information. I go back to that one because I am trying to improve knowing my nutritional intake. (Disclaimer, I made this GPT: https://chat.openai.com/g/g-IejfE7Hpb-nutrition-tracker)

How did you create the prompt to give concise answers? Looks great!

I work for determined.ai, glad you find it useful! Feel free to reach out if you need any help or questions with Determined. The examples are the best resources IMO to configure your models and data to work in determined.

If you could search through github projects (torchvision, mmdetection, etc) and index high quality pytorch snippets, that would be awesome. I learn a lot about how to manipulate tensors and achieving specific deep learning tasks from GitHub projects, that’s how I answer various questions on how to implement key ideas with pytorch.

For anomaly detection, does your team have to worry about concept drift and data distribution shifts? If so, how do you combat that?

Amazing work! Is it possible to finetune this model on your own code, or a subset? Finetuning this model on pytorch code to help with tensor manipulation would be awesome!

Are there any good recipe search engines that are below 8Mb? Would be cool to create an on device application where space is really limited!

The city. I considered the MARC, but I have to go out to Herndon, VA and Fairfax, VA area so its just more convenient to drive. It's a tough commute at times, but a good podcast gets me through the drive. I have been WFH for all of 2020 an 2021, so now I just stay in Baltimore. WFH shift is working out great!

I am living in Baltimore and work out of DC as an ML engineer. The commute sucks, but with because of covid, I have been WFH. DC has a lot of tech companies, but lots of connections to defense.

A lot of these models are open source, I wonder who would this product market towards?

Wow, this is really cool! Is inference running on GPU or CPU? I wonder how well this would run if model storage and inference is done on a jetson nano!

This is really cool! I wonder how the performance would be on a Jetson Nano!

This looks super cool!

Your comment made me laugh! I can imagine a machine learning model would derive the same meaning.

Any advice on breaking into the industry? Would be really neat to work as a quantum research engineer, or software engineer on quantum computers!

Love the concept. I have been in need for a tool/service like this for a while. I now resort to creating my own slack workspace and using channels as a way to save links. Using chrome plugins is a crappy alternative. I don’t know if I would pay for it though. I would be amazing if after all the links you add, the system “predicts” or “recommends” what other links would be a good fit in this category, or help you auto organize better.