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Updated: 26 min 29 sec ago

Show HN: MongoDB MCP Server

Sat, 03/29/2025 - 3:11am

A Model Context Protocol server that provides access to MongoDB databases. This server enables LLMs to inspect collection schemas and execute MongoDB operations.

Comments URL: https://news.ycombinator.com/item?id=43513465

Points: 1

# Comments: 0

Categories: Hacker News

0926364544

Sat, 03/29/2025 - 3:00am

Comments URL: https://news.ycombinator.com/item?id=43513410

Points: 1

# Comments: 0

Categories: Hacker News

Can CRUD softwares survive in this AI era?

Sat, 03/29/2025 - 2:35am

Had a funny incident recently:

I created a clone of a SaaS product and shared it with the founder and he was pissed (scale, security etc etc), This led me to think, is there any product moat left in SaaS (I know distribution is there).

With time do you'll think that every department can create AI enabled internal softwares needed for their operations ?

Do you think organization's would want to shift from per user pricing model to a fixed maintenance cost for their in house softwares ?

Comments URL: https://news.ycombinator.com/item?id=43513304

Points: 3

# Comments: 2

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Andy Warhol's Sacraments

Sat, 03/29/2025 - 2:22am
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Eightyeightthirtyone

Sat, 03/29/2025 - 2:15am

Article URL: https://eightyeightthirty.one/

Comments URL: https://news.ycombinator.com/item?id=43513220

Points: 1

# Comments: 3

Categories: Hacker News

Ente Photos v1

Sat, 03/29/2025 - 2:05am

Article URL: https://ente.io/blog/v1/

Comments URL: https://news.ycombinator.com/item?id=43513176

Points: 1

# Comments: 0

Categories: Hacker News

DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products

Sat, 03/29/2025 - 1:53am

https://openreview.net/forum?id=nvb60szj5C

Twitter / X: https://x.com/julien_siems/status/1905628609714286687

Authors: Julien Siems, Timur Carstensen, Arber Zela, Frank Hutter, Massimiliano Pontil, Riccardo Grazzi* (*equal contribution)

Abstract: Linear Recurrent Neural Networks (linear RNNs) have emerged as competitive alternatives to Transformers for sequence modeling, offering efficient training and linear-time inference. However, existing architectures face a fundamental trade-off between expressivity and efficiency, dictated by the structure of their state-transition matrices. While diagonal matrices used in architectures like Mamba, GLA, or mLSTM yield fast runtime, they suffer from severely limited expressivity. To address this, recent architectures such as (Gated) DeltaNet and RWKV-7 adopted a diagonal plus rank-1 structure, allowing simultaneous token-channel mixing, which overcomes some expressivity limitations with only a slight decrease in training efficiency. Building on the interpretation of DeltaNet's recurrence as performing one step of online gradient descent per token on an associative recall loss, we introduce DeltaProduct, which instead takes multiple (nh) steps per token. This naturally leads to diagonal plus rank-state-transition matrices, formed as products of nh generalized Householder transformations, providing a tunable mechanism to balance expressivity and efficiency and a stable recurrence. Through extensive experiments, we demonstrate that DeltaProduct achieves superior state-tracking and language modeling capabilities while exhibiting significantly improved length extrapolation compared to DeltaNet. Additionally, we also strengthen the theoretical foundation of DeltaNet by proving that it can solve dihedral group word problems in just two layers.

Comments URL: https://news.ycombinator.com/item?id=43513111

Points: 1

# Comments: 0

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