Hacker News

Article URL: https://medium.com/@leandrocastelluccio/unlocking-the-depths-of-human-existence-an-invitation-to-engage-with-propositions-0df3fbed23d9

Comments URL: https://news.ycombinator.com/item?id=41557664

Points: 1

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Article URL: https://domainincite.com/30251-big-twist-as-icann-bans-new-gtld-auctions

Comments URL: https://news.ycombinator.com/item?id=41557659

Points: 2

# Comments: 0

Article URL: https://gistpreview.github.io/?b5dc899c98232b45540696e937467645

Comments URL: https://news.ycombinator.com/item?id=41557653

Points: 1

# Comments: 0

Article URL: https://thezvi.substack.com/p/gpt-4o1

Comments URL: https://news.ycombinator.com/item?id=41557645

Points: 2

# Comments: 0

Article URL: https://freddiedeboer.substack.com/p/to-learn-to-live-in-a-mundane-universe

Comments URL: https://news.ycombinator.com/item?id=41557641

Points: 1

# Comments: 1

Article URL: https://arnoldkling.substack.com/p/roundabout-production

Comments URL: https://news.ycombinator.com/item?id=41557637

Points: 1

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Article URL: https://insideclimatenews.org/news/15092024/foreign-investor-legal-claims-threaten-to-bankrupt-honduras/

Comments URL: https://news.ycombinator.com/item?id=41557628

Points: 2

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Article URL: https://abc13.com/watch/live/

Comments URL: https://news.ycombinator.com/item?id=41557064

Points: 1

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Article URL: https://twitter.com/JonErlichman/status/1835386407369719951

Comments URL: https://news.ycombinator.com/item?id=41557062

Points: 1

# Comments: 0

Article URL: https://www.koyeb.com/blog/new-dashboard-build-run-and-scale-apps-in-minutes-with-a-simple-and-elegant-interface

Comments URL: https://news.ycombinator.com/item?id=41557061

Points: 1

# Comments: 0

Thinkercan is a tool that leverages AI and a Computer Algebra System (CAS) to solve math problems step-by-step. The AI reasons the problem and determines when to apply CAS for solving or simplifying. Each solution is presented in a detailed, educational format to enhance understanding like photomath and wolfram.

It’s designed to support both basic and advanced math problems, offering insight into the problem-solving process.

Looking forward to your feedback.

https://thinkercan.com/

Comments URL: https://news.ycombinator.com/item?id=41557038

Points: 1

# Comments: 0

Article URL: https://www.bedframe.dev/blog/dont-just-commit-precommit

Comments URL: https://news.ycombinator.com/item?id=41557037

Points: 1

# Comments: 0

I've been learning about the L4 microkernel, and am thinking about doing something related to it for a research project. I'm especially curious about more recent examples of specific devices that run L4 variants (seL4, PikeOS, OKL4, etc.). I already found a few that use seL4, but to take OKL4 as an example, most of the specific devices I could find are from more than a decade ago, and I'm trying to find things from at least the last 5 or 6 years. I'm even more curious to find devices that use a form of L4 as a hypervisor.

Has anyone here worked on a device that used an L4-related kernel or hypervisor? I know one major area they're used in is defense and full of NDAs, but hopefully some of the other industries they're used in (medical devices, automotive, IoT) are a little less restrictive. Thanks in advance!

Comments URL: https://news.ycombinator.com/item?id=41557030

Points: 2

# Comments: 0

Article URL: https://www.nasa.gov/directorates/stmd/prizes-challenges-crowdsourcing-program/center-of-excellence-for-collaborative-innovation-coeci/nasa-airport-throughput-prediction-challenge/

Comments URL: https://news.ycombinator.com/item?id=41557026

Points: 1

# Comments: 0

Hi HN!

We're Gabriel & Viraj, and we're excited to open source TensorZero.

To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products.

1. Integrate our model gateway

2. Send metrics or feedback

3. Unlock compounding improvements in quality, cost, and latency

It enables a data & learning flywheel for LLMs by unifying:

• Inference: one API for all LLMs, with <1ms P99 overhead

• Observability: inference & feedback → your database

• Optimization: better prompts, models, inference strategies

• Experimentation: built-in A/B testing, routing, fallbacks

Our goal is to help engineers build, manage, and optimize the next generation of LLM applications: AI systems that learn from real-world experience.

In addition to a Quick Start (5min) [1] and a Tutorial (30min) [2], we've also published a series of complete runnable examples illustrating TensorZero's data & learning flywheel.

• Writing Haikus to Satisfy a Judge with Hidden Preferences [3] – my personal favorite

• Fine-Tuning TensorZero JSON Functions for Named Entity Recognition (CoNLL++) [4]

• Automated Prompt Engineering for Math Reasoning (GSM8K) with a Custom Recipe (DSPy) [5]

___

[1] https://www.tensorzero.com/docs/gateway/quickstart

[2] https://www.tensorzero.com/docs/gateway/tutorial

[3] https://github.com/tensorzero/tensorzero/tree/main/examples/...

[4] https://github.com/tensorzero/tensorzero/tree/main/examples/...

[5] https://github.com/tensorzero/tensorzero/tree/main/examples/...

We hope you find TensorZero useful! Feedback and questions are very welcome. If you're interested in using it at work, we'd be happy to set up a Slack channel with your team (free).

Comments URL: https://news.ycombinator.com/item?id=41557020

Points: 9

# Comments: 0

Article URL: http://vickash.com/2024/09/15/bit_bang_spi_with_ruby-yjit.html

Comments URL: https://news.ycombinator.com/item?id=41557014

Points: 1

# Comments: 0

We plan to run a competition with a prize pool as an experimental way of finding amazing candidates. Has anyone seen this work out?

For the top submissions we will extend an offer to join the team.

For the winning submission, we also plan to ship their solution into Danswer so that their work has impact beyond the competition. The goal will be to create a solution for query expansion to use in RAG. To be able to actually productionize it and to encourage more creative thinking (can’t just finetune a pretrained encoder decoder transformer), we have a few requirements:

[1] If it’s deep learning based, it must be capable of running on CPU (up to 50 tokens in 200ms or less on a 4 cpu core machine) [2] It must not rely on any proprietary or restrictively licensed software

I know there is Kaggle but I don’t believe they allow constraints like the above ^

What is the best way to run a competition like this?

Comments URL: https://news.ycombinator.com/item?id=41556983

Points: 1

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Article URL: https://50games.fun/

Comments URL: https://news.ycombinator.com/item?id=41556982

Points: 1

# Comments: 0

Article URL: https://www.startupdispatch.com/the-best-programming-language-for-startups/

Comments URL: https://news.ycombinator.com/item?id=41556962

Points: 1

# Comments: 0

Article URL: https://www.phoronix.com/review/ampereone-clang-gcc

Comments URL: https://news.ycombinator.com/item?id=41556958

Points: 2

# Comments: 0