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164 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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"We used Opik, an open-source tool made by Comet, as our prompt monitoring tool because it follows Comet’s philosophy of simplicity and ease of use, which is currently relatively rare in the LLM landscape."
Shots fired! from H2 of the LLM Engineer's handbook by Maxime Labonne and Paul Iusztin.
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"We used Opik, an open-source tool made by Comet, as our prompt monitoring tool because it follows Comet’s philosophy of simplicity and ease of use, which is currently relatively rare in the LLM landscape."
Shots fired! from H2 of the LLM Engineer's handbook by Maxime Labonne and Paul Iusztin.
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"We used Opik, an open-source tool made by Comet, as our prompt monitoring tool because it follows Comet’s philosophy of simplicity and ease of use, which is currently relatively rare in the LLM landscape."
Shots fired! from H2 of the LLM Engineer's handbook by Maxime Labonne and Paul Iusztin.
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"We used Opik, an open-source tool made by Comet, as our prompt monitoring tool because it follows Comet’s philosophy of simplicity and ease of use, which is currently relatively rare in the LLM landscape."
Shots fired! from H2 of the LLM Engineer's handbook by Maxime Labonne and Paul Iusztin.
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"We used Opik, an open-source tool made by Comet, as our prompt monitoring tool because it follows Comet’s philosophy of simplicity and ease of use, which is currently relatively rare in the LLM landscape."
Shots fired! from H2 of the LLM Engineer's handbook by Maxime Labonne and Paul Iusztin.
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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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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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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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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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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! 🙌