Liquid AI Introduces LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M: Dense Bi-Encoder and Late-Interaction Models for Fast Multilingual Search Across 11 Languages

This week, Liquid AI released two new retrieval models. They are LFM2.5-ColBERT-350M and LFM2.5-Embedding-350M. Both hold 350M parameters. Both are the first bidirectional members of the LFM family. They build on LFM2.5-350M-Base, released in March. The pair targets fast multilingual and cross-lingual search across 11 languages. Their footprint is small enough to run almost anywhere….

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The US says ASML’s prime chip software could also be in China. ASML says it is not

The US says ASML’s prime chip software could also be in China. ASML says it is not

In response to Bloomberg, U.S. Commerce Secretary Howard Lutnick has, in a collection of current conferences, instructed senior ASML executives he’s involved that one of many Dutch chipmaker’s excessive ultraviolet lithography machines — the EUV programs which might be the one instruments on Earth able to printing essentially the most superior semiconductor patterns — may…

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Rivian house owners file lawsuit alleging false guarantees on self-driving options

Rivian house owners file lawsuit alleging false guarantees on self-driving options

Rivian has been sued over allegations that the EV maker made false claims concerning the autonomous driving capabilities of its R1T truck and R1S SUV fashions. The category-action criticism, which was filed Wednesday within the U.S. District Courtroom for the Central District of California, focuses on the first-generation fashions of the R1T and R1S and…

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Salesforce CodeGen Tutorial: Generate, Validate, and Rerank Python Functions With Unit Tests and Safety Checks

def extract_function_source(full_text, function_name): text = full_text.replace(“\r\n”, “\n”) fence = re.search(r”“`(?:python)?\n(.*?)“`”, text, flags=re.S | re.I) if fence: text = fence.group(1) pattern = rf”^def\s+{re.escape(function_name)}\s*\(” match = re.search(pattern, text, flags=re.M) if not match: return “” chunk = text[match.start():] lines = chunk.splitlines() collected = [] for i, line in enumerate(lines): if i > 0: if line.startswith(“def “) or line.startswith(“class…

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