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119 results for “jd7h”
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This is a handy list for comparing the features of vector databases (holy mole there are a lot of them), including year of launch, opensource-ness, licences, and implementation language: https://superlinked.com/vector-db-comparison
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This is a handy list for comparing the features of vector databases (holy mole there are a lot of them), including year of launch, opensource-ness, licences, and implementation language: https://superlinked.com/vector-db-comparison
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This is a handy list for comparing the features of vector databases (holy mole there are a lot of them), including year of launch, opensource-ness, licences, and implementation language: https://superlinked.com/vector-db-comparison
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This is a handy list for comparing the features of vector databases (holy mole there are a lot of them), including year of launch, opensource-ness, licences, and implementation language: https://superlinked.com/vector-db-comparison
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This is a handy list for comparing the features of vector databases (holy mole there are a lot of them), including year of launch, opensource-ness, licences, and implementation language: https://superlinked.com/vector-db-comparison
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Generative AI apps have their own version of the training-serving skew from classical ML: the eval-production gap.
You create an eval dataset, optimize your LLM flows against it, hit great performance on your metrics, and ship. Then real users show up and:
- Write input texts of multiple pages long
- Ask in Spanish, Russian or Chinese when you tested in English
- Upload file types you never considered
- Ask questions from domains your product wasn't designed for -
This is a neat solution for those old Python projects that have no uv, pyproject.toml, or version-pinned requirements.txt. It allows you to go "back in time" with pip!
https://pypi.org/project/pypi-timemachine/
Edit: @bk1e pointed out pip >= 26 has this option built-in. Use `--uploaded-prior-to `!
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Sooooo is there a special name for Bluesky posts? And what's the social protocol for ~~retweeting~~ boosting them on Mastodon?
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Sooooo is there a special name for Bluesky posts? And what's the social protocol for ~~retweeting~~ boosting them on Mastodon?
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Sooooo is there a special name for Bluesky posts? And what's the social protocol for ~~retweeting~~ boosting them on Mastodon?
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Sooooo is there a special name for Bluesky posts? And what's the social protocol for ~~retweeting~~ boosting them on Mastodon?
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Sooooo is there a special name for Bluesky posts? And what's the social protocol for ~~retweeting~~ boosting them on Mastodon?
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@lucasmeijer has given an introductory talk about pi.dev. The first half is a pretty good summary of what I've learned in the past 3 months about working with coding agents. The second half made me even more curious about pi.
Lucas' exasperated "Claude, the answer to question 16 is YES" cracked me up. 😆
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@lucasmeijer has given an introductory talk about pi.dev. The first half is a pretty good summary of what I've learned in the past 3 months about working with coding agents. The second half made me even more curious about pi.
Lucas' exasperated "Claude, the answer to question 16 is YES" cracked me up. 😆
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We were looking for a local tokenizer for counting the number of input tokens before calling the gemini-embedding-001 endpoint on vertex AI. Turns out this Gemma tokenizer returns exactly the same number of tokens as the usage in the embeddings result `embedding.statistics.token_count` of the Gemini embeddings endpoint. Tested on 2000 datapoints. 😁
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We were looking for a local tokenizer for counting the number of input tokens before calling the gemini-embedding-001 endpoint on vertex AI. Turns out this Gemma tokenizer returns exactly the same number of tokens as the usage in the embeddings result `embedding.statistics.token_count` of the Gemini embeddings endpoint. Tested on 2000 datapoints. 😁
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We were looking for a local tokenizer for counting the number of input tokens before calling the gemini-embedding-001 endpoint on vertex AI. Turns out this Gemma tokenizer returns exactly the same number of tokens as the usage in the embeddings result `embedding.statistics.token_count` of the Gemini embeddings endpoint. Tested on 2000 datapoints. 😁
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We were looking for a local tokenizer for counting the number of input tokens before calling the gemini-embedding-001 endpoint on vertex AI. Turns out this Gemma tokenizer returns exactly the same number of tokens as the usage in the embeddings result `embedding.statistics.token_count` of the Gemini embeddings endpoint. Tested on 2000 datapoints. 😁
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We were looking for a local tokenizer for counting the number of input tokens before calling the gemini-embedding-001 endpoint on vertex AI. Turns out this Gemma tokenizer returns exactly the same number of tokens as the usage in the embeddings result `embedding.statistics.token_count` of the Gemini embeddings endpoint. Tested on 2000 datapoints. 😁
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I was a guest at BNR's De Technoloog, to talk about the latest in LLMs, vibecoding and AI-native startups.
Podcast interview (in Dutch): https://www.bnr.nl/podcast/de-technoloog/10597036/de-duct-tape-fase-van-ai
#deTechnoloog #BNR #llms #genai #podcast #vibecoding #claudecode
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The related top HN comment is also worth reading: https://news.ycombinator.com/item?id=47491023
"You're comparing [DSPy] downloads with Langchain, probably the worst package to gain popularity of the last decade. It was just first to market, then after a short while most realized it's horrifically architected, and now it's just coasting on former name recognition while everyone who needs to get shit done uses something lighter like the above two."
Preach! 🙌
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If you disregard the "DSPy is my favorite hammer and every LLM workflow project is a nail" theme, this blogpost paints a good picture of the natural evolution of LLM engineering at startups with a generative AI product:
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Pretty cool write-up about building a receptionist LLM workflow for a car mechanic. I can definitely see this working with Claude Sonnet and an ElevenLabs voice -- although I would also love to redteam it and see where the flaws are.
https://www.itsthatlady.dev/blog/building-an-ai-receptionist-for-my-brother/
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TIL #PyAI on March 10th 2026 (just missed it). Small event, focused on unglamourous AI in production, some of the speakers were practitioners I know and respect. The description reminds me a bit of #NormConf !
- Talk videos will hopefully be released online soon
- Blogpost by @pamelafox, one of the speakers: https://blog.pamelafox.org/2026/03/learnings-from-pyai-conference.html
- Organisers plan to organize another one next year 👀 -
I used #Pydantic Evals to evaluate a bunch of agents today. After running an evaluation, I'd like to inspect the SpanTree for each evaluation case, e.g. to check which tools were called and debug my custom Evaluators. My current approach is a custom Evaluator that captures the tree as a side effect into a module-level variable.
Storing the trees in a global var is not great, so let's see if we can come up with a better solution: https://github.com/pydantic/pydantic-ai/issues/4758
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Planning to make large behavioural changes to a (sometimes long-running) production-grade AI agent. Working with `pydantic-evals` today because I want to eval the agent before and after. So far it looks very similar to Langfuse datasets/runs for evalling, except that the data lives in your repository instead of in the Langfuse platform.
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Hahaha, oh Pydantic...
> Unlike unit tests, evals are an emerging art/science. Anyone who claims to know exactly how your evals should be defined can safely be ignored.
Source: https://ai.pydantic.dev/evals/
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New Mosterdgeel recipe for Pi-day: Banana bread from a French cryptographer
https://www.mosterdgeel.nl/recepten/bananenbrood/ (in Dutch)
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New Mosterdgeel recipe for Pi-day: Banana bread from a French cryptographer
https://www.mosterdgeel.nl/recepten/bananenbrood/ (in Dutch)
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New Mosterdgeel recipe for Pi-day: Banana bread from a French cryptographer
https://www.mosterdgeel.nl/recepten/bananenbrood/ (in Dutch)