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stephantul

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NLP engineer

Posts21
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stephantul.github.io 1mo ago

From Chesterton's fence to Chesterton's gap

stephantul
86pts56
stephantul.github.io 2mo ago

Why scikit learn's fit transform is probably not for you

stephantul
1pts0
github.com 2mo ago

Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep

stephantul
8pts0
github.com 2mo ago

Show HN: Semble – Fast code search for agents with near-transformer accuracy

stephantul
7pts0
github.com 5mo ago

Show HN: Skeletoken, a Python package for editing model tokenizers

stephantul
1pts0
github.com 8mo ago

Show HN: PyNIFE. 400-900× speedup for embedding-based retrieval pipelines

stephantul
2pts0
github.com 10mo ago

Show HN: Skeletoken, a Package for Editing Tokenizers

stephantul
1pts0
stephantul.github.io 11mo ago

Turning any tokenizer into a greedy one

stephantul
2pts1
stephantul.github.io 11mo ago

Decasing Transformers for Fun

stephantul
3pts1
minish.ai 11mo ago

Model2Vec as a Fasttext Alternative

stephantul
5pts1
stephantul.github.io 1y ago

Using overloads to handle union return types in Python

stephantul
1pts1
news.ycombinator.com 1y ago

Ask HN: Favourite resources for learning programming type theory?

stephantul
6pts8
stephantul.github.io 1y ago

Evaluating ML classifiers using relative error instead of absolute accuracy

stephantul
1pts0
stephantul.github.io 1y ago

Defeat stringly typing without making your users unhappy

stephantul
2pts0
minishlab.github.io 1y ago

Distilling ModernBERT into a static model doesn't work

stephantul
5pts3
github.com 1y ago

Show HN: SemHash – Fast Semantic Text Deduplication for Cleaner Datasets

stephantul
6pts0
huggingface.co 1y ago

Train faster static embedding models with sentence transformers

stephantul
52pts1
minishlab.github.io 1y ago

Semhash: Fast deduplication and dataset multitool in Python

stephantul
3pts1
huggingface.co 1y ago

Model2Vec: Make sentence transformers 500x faster on CPU, 15x smaller

stephantul
5pts0
github.com 1y ago

Show HN: Model2Vec: make sentence transformers 500x faster on CPU, 15x smaller

stephantul
9pts2
github.com 1y ago

Show HN: Model2Vec: make sentence transformers 500x faster on CPU, 15x smaller

stephantul
6pts2

I think this is such an nefariously unnecessary negative argument.

Most, if not all, of the shai-hulud attacks that hit npm and other ecosystems were preventable with cooldowns. And these were not detected because regular users reported the worms, but because security researchers did. I don’t think I’ve ever seen an attack that was discovered because a user reported it.

Sadly 100% generated.

I think the idea is interesting though, although I wonder if training time for LoRA is such a bottleneck to deserve its own, extremely narrowly scoped, leaderboard. Maybe if it was more tasks or more models we could hope that it transfers? With a single task, and a single model, I’d be afraid of this overfitting pretty heavily.

For NanoGPT, I think the idea always was that the ideas can be transferred to much larger models, or serve as stepping stones for investigations on larger models.

I think he is a good example of someone who writes mainly to show he is ready for the next rung of the corporate ladder. That is, his posts are not meant to be useful, but to show higher-ups he is useful to them.

I am interested in why you chose to do this, and publish it with the headline you used. Was it to learn something? Or to get publicity for another project?

Tbh, this sounds like fear mongering to me. Of course the statement “99.9% of servers are not compliant” sounds impressive, but then it turns spec hasn’t even been released yet.

Also some general feedback: the whole thing looks generated, as does the comment I am replying to.

OpenRA 26 days ago

Thanks for introducing me to the article! I’ve experienced this myself but didn’t know it had a name.

The second part of this comment is not what I expected. I also don’t think it is true. I got bit by a CORS error at work recently that passed by Claude, copilot, and another senior engineer.

We’ve been on the receiving end of this complaint with Semble. I think it is a valid complaint, but constructing a benchmark for this kind of thing is just very difficult and expensive because of the (harness) x (model) x (mcp/cli) combination.

With traditional ml/tooling, not showing benchmarks was usually a red flag. But for llm tooling, I’m not so sure.

I’m not sure I share your view of PRs. I still see submitting PRs as something that puts pressure on maintainers. Even incorrect PRs take time to verify and review.

I also don’t see how this differs between the “gap” and the “fence” part of the metaphor. Whether someone submits a rewrite/removal (fence) or a new feature (gap) for PR review, it’s still going to cost me attention.

It’s an interesting question: I’d say this is more of a vulnerability creator than the actual vulnerability.

Similar to how using very difficult technologies makes you more likely to create code with vulnerabilities: the technologies are not the vulnerability, but it’s easier to cause them.

Sure, the whole premise is exactly that proof of work reduces the value of scraping, while having negligible impact on users. If the data is so valuable that bot operators are willing to pay 10s of cpu, then other measures are necessary.

Nevertheless even for these high value cases, you can still argue that it disincentivizes the business model, it becomes less efficient.

I was also at the event and was pretty disappointed. Most of the talks were pretty low on information. I was at the “build” stage, which supposedly was the technical stage, but the talks there didn’t really go into technical specifics.

The papyrus talk was awesome though.