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The radix 2^51 trick (2017)

https://www.chosenplaintext.ca/articles/radix-2-51-trick.html
230•blobcode•7h ago•35 comments

Radio Astronomy Software Defined Radio (Rasdr)

https://radio-astronomy.org/rasdr
24•zeristor•2h ago•4 comments

Tokenization for language modeling: BPE vs. Unigram Language Modeling (2020)

https://ndingwall.github.io/blog/tokenization
16•phewlink•2h ago•0 comments

Bridged Indexes in OrioleDB: architecture, internals and everyday use?

https://www.orioledb.com/blog/orioledb-bridged-indexes
17•pella•1h ago•1 comments

What Happens When AI-Generated Lies Are More Compelling Than the Truth?

https://behavioralscientist.org/what-happens-when-ai-generated-lies-are-more-compelling-than-the-truth/
14•the-mitr•1h ago•6 comments

Atomics and Concurrency

https://redixhumayun.github.io/systems/2024/01/03/atomics-and-concurrency.html
20•LAC-Tech•2d ago•2 comments

Turn a Tesla into a mapping vehicle with Mapillary

https://blog.mapillary.com/update/2020/12/09/map-with-your-tesla.html
42•faebi•1d ago•15 comments

Practical SDR: Getting started with software-defined radio

https://nostarch.com/practical-sdr
165•teleforce•10h ago•43 comments

Triangle splatting: radiance fields represented by triangles

https://trianglesplatting.github.io/
93•ath92•7h ago•38 comments

WeatherStar 4000+: Weather Channel Simulator

https://weatherstar.netbymatt.com/
623•adam_gyroscope•20h ago•115 comments

FLUX.1 Kontext

https://bfl.ai/models/flux-kontext
395•minimaxir•17h ago•100 comments

Why do we get earworms?

https://theneuroscienceofeverydaylife.substack.com/p/mahna-mahna-do-doo-be-do-do-why-do
8•lentoutcry•2h ago•8 comments

Show HN: MCP Server SDK in Bash (~250 lines, zero runtime)

https://github.com/muthuishere/mcp-server-bash-sdk
74•muthuishere•7h ago•21 comments

Printing metal on glass with lasers [video]

https://www.youtube.com/watch?v=J0NNO91WyXM
7•surprisetalk•2d ago•1 comments

OpenBAO (Vault open-source fork) Namespaces

https://openbao.org/blog/namespaces-announcement/
44•gslin•8h ago•19 comments

Dr John C. Clark, a scientist who disarmed atomic bombs twice

https://daxe.substack.com/p/disarming-an-atomic-bomb-is-the-worst
99•vinnyglennon•2d ago•64 comments

The atmospheric memory that feeds billions of people: Monsoon rainfall mechanism

https://phys.org/news/2025-05-atmospheric-memory-billions-people-monsoon.html
28•PaulHoule•2d ago•6 comments

Buttplug MCP

https://github.com/ConAcademy/buttplug-mcp
184•surrTurr•4h ago•98 comments

Show HN: I wrote a modern Command Line Handbook

https://commandline.stribny.name/
353•petr25102018•20h ago•92 comments

Smallest Possible Files

https://github.com/mathiasbynens/small
43•yread•2d ago•16 comments

Player Piano Rolls

https://omeka-s.library.illinois.edu/s/MPAL/page/player-piano-rolls-landing
46•brudgers•8h ago•30 comments

How to Do Ambitious Research in the Modern Era [video]

https://www.youtube.com/watch?v=w7DVlI_Ztq8
32•surprisetalk•6h ago•1 comments

Show HN: templUI – The UI Kit for templ (CLI-based, like shadcn/UI)

https://templui.io/
39•axadrn•7h ago•20 comments

Superauthenticity: Computer Game Aspect Ratios

https://datadrivengamer.blogspot.com/2025/05/superauthenticity-computer-game-aspect.html
15•msephton•3d ago•5 comments

Show HN: Donut Browser, a Browser Orchestrator

https://donutbrowser.com/
44•andrewzeno•7h ago•21 comments

Making C and Python Talk to Each Other

https://leetarxiv.substack.com/p/making-c-and-python-talk-to-each
121•muragekibicho•3d ago•75 comments

Why is everybody knitting chickens?

https://ironicsans.ghost.io/why-is-everybody-knitting-chickens/
139•mooreds•2d ago•104 comments

I'm starting a social club to solve the male loneliness epidemic

https://wave3.social
215•nswizzle31•11h ago•412 comments

White House MAHA Report may have garbled science by using AI

https://www.washingtonpost.com/health/2025/05/29/maha-rfk-jr-ai-garble/
6•ctippett•49m ago•1 comments

Notes on Tunisia

https://mattlakeman.org/2025/05/29/notes-on-tunisia/
86•returningfory2•14h ago•42 comments
Open in hackernews

Domain Adaptation of Base Models + ShadowdarkQA Bench

https://gygaxtest.com/posts/continued_pretraining_for-rules/
17•pact_inference•21h ago

Comments

palmfacehn•21h ago
Isn't this a use case for a RAG?
pact_inference•21h ago
definitely! However, my intuition is that correctly interpreting the rules pulled in context will require some basic understanding of the game system that pretraining would help with. Ultimately after training this base model for instruction-tuning and tool-use (to provide a search tool) I'll compare it against https://huggingface.co/Qwen/Qwen3-0.6B without any specific domain pretraining and see how it performs at rule adjudication. I expect the shadowdark-trained model will have better understanding of the rules, but there's only one way to find out.
palmfacehn•20h ago
It is an interesting problem to solve. When reading, I noticed the model's ambiguity around terms like 4d6. At first I thought you might try editing your markup to describe the concept of dice more thoroughly. Ultimately I wonder if you might try having the model fill in data to be utilized by a hard coded combat system. Are you going to rely on the LLM for pseudorandom numbers? Concepts like turns and dice rolls could be abstractly defined in code and instantiated by the model.

The model might excel at creating character sheets, after you define a schema. From there you can validate the generated sheets against known lore. You could combine the story telling from the LLM with the formalized character schema to create campaigns. I'm not an expert here, but I suspect you might try asking the model to translate an existing fantasy story dataset into a series of narration/dialogue blocks and character sheets.

Without training, I've experimented with similar approaches for item generation using EBNF.

pact_inference•20h ago
> Are you going to rely on the LLM for pseudorandom numbers?

Definitely! I'm going to start with instruction tuning it for basic question answering, and then add tools to allow it to search the markdown source to cite answers to rules questions. I think adding some dice tooling for proper character sheet creation would be an awesome task to test as well. I'm actually thinking a lot about what tasks I could try that are "trivially" programmatically verifiable in their correctness for stuff like GRPO, so I'm definitely going to use that idea.

> You could combine the story telling from the LLM with the formalized character schema to create campaigns. I'm not an expert here, but I suspect you might try asking the model to translate an existing fantasy story dataset into a series of narration/dialogue blocks and character sheets.

I think probably late this year I'll be able to work on that sort of thing. There's a really interesting approach to story generation https://arxiv.org/abs/2503.22828 here, but modifying ways to translate it into campaign relevant structured objects and "reward" that will take some experimentation.

jasonjmcghee•20h ago
> I used the AdamW optimizer and selected a learning rate of 5e-5. I’ve seen learning rates of 5e-6 for pretraining and 5e-5 for finetuning. I would consider this closer to the latter - I don’t want to totally destroy the knowledge Qwen already had, I just want to add to it a bit.

Is this a typo? Maybe 5e-4 for pretraining?

Otherwise this goes against all the intuition I have around learning rates and catastrophic forgetting. (a smaller learning rate causing knowledge degredation)

pact_inference•20h ago
whoops, definitely a typo! It should be 5e-4 for as the base "pretraining" LR, you're absolutely correct.

your intuition is sound, but my fingers are not.